搜索结果排序方法、装置及电子设备

Search result ordering method and device and electronic equipment

文档序号：7858 发布日期：2021-09-17 浏览：31次中文

1. A method for ranking search results, comprising:

searching and acquiring N enterprises based on the index information; wherein N is an integer greater than or equal to 2;

determining N ranking values corresponding to the N enterprises;

screening M enterprises meeting a first preset popularity condition from the N enterprises; wherein M is an integer less than or equal to N;

adjusting M ranking values corresponding to the M enterprises;

and sorting the N enterprises according to the adjusted M sorting values and the rest N-M sorting values in the N sorting values.

2. The method of claim 1, wherein the adjusting the M ranking values for the M businesses comprises:

calculating the sum of a first preset value and a target ranking value corresponding to a target enterprise in the N ranking values; wherein the target enterprise is any one of the M enterprises;

and adjusting the target sorting value according to the sum value.

3. The method of claim 2, wherein said adjusting the target rank value according to the sum comprises:

determining an additional value of the target enterprise according to the frequency of first preset events on the enterprise information of the target enterprise and the frequency of the first preset events on the enterprise information of the target enterprise displayed on the basis of the index information before N enterprises are searched and obtained on the basis of the index information;

calculating the sum of the additional value and the sum value;

updating the target rank value to the sum.

4. The method of claim 1, wherein the index information includes target person name information;

the screening of M enterprises meeting a first preset popularity condition from the N enterprises includes:

acquiring a preset known enterprise set corresponding to the target name information;

and screening at least one enterprise in the set of known enterprises from the N enterprises to serve as M enterprises meeting a first preset awareness condition.

5. The method according to claim 4, wherein the obtaining of the preset known enterprise set corresponding to the target name information includes:

determining the number Q of target persons having the target person name information; wherein Q is an integer greater than or equal to 1;

under the condition that Q is 1, determining each enterprise, in a preset associated enterprise set corresponding to the target person, of which the frequency of second preset events on enterprise information is greater than the first preset frequency, and forming a preset known enterprise set corresponding to the target person name information by all the determined enterprises;

and under the condition that Q is larger than 1, obtaining Q popularity values of Q target characters, and determining a preset popularity enterprise set corresponding to the target character information according to the Q popularity values and at least part of preset related enterprise sets corresponding to the Q target characters.

6. The method of claim 5, wherein the determining the preset known business set corresponding to the target person name information according to the Q popularity values and at least part of the preset Q related business sets corresponding to the Q target persons comprises:

screening all the target people with the highest popularity values to the Rth highest from Q target people under the condition that the difference value between the popularity value of the first specific height and the popularity value of the second specific height in the Q popularity values is smaller than or equal to a preset difference value; wherein R is an integer less than or equal to Q;

determining a person set from all the screened target persons; the difference value of the awareness values of any two adjacent target people in the people set in height is smaller than or equal to the preset difference value;

merging preset associated enterprise sets corresponding to all the target characters in the character set to obtain a merged enterprise set;

carrying out duplicate removal processing on the merged enterprise set;

and determining each enterprise of which the frequency of the second preset event occurring on the enterprise information is greater than the first preset frequency in the merged enterprise set after the duplication removal processing, and forming a preset known enterprise set corresponding to the target name information by all the determined enterprises.

7. The method of claim 5, wherein the determining the preset known business set corresponding to the target person name information according to the Q popularity values and at least part of the preset Q related business sets corresponding to the Q target persons comprises:

screening the target person with the highest popularity value from the Q target persons under the condition that the difference value between the popularity value of the first specific height and the popularity value of the second specific height in the Q popularity values is larger than a preset difference value;

and determining the preset associated enterprise set corresponding to the screened target person as a preset known enterprise set corresponding to the target person name.

8. The method of claim 1, wherein the index information includes target person name information;

the method further comprises the following steps:

acquiring feature information of a target person with the target person name information, and acquiring the frequency of a third preset event on the person information of the target person;

judging whether the target person meets a second preset popularity condition or not according to the feature information and the frequency of third preset events on the person information;

under the condition that the target person meets the second preset popularity condition, the step of screening M enterprises meeting the first preset popularity condition from the N enterprises is executed;

and under the condition that the target person does not meet the second preset popularity condition, sorting the N enterprises according to the N sorting values.

9. The method of claim 8, wherein the determining whether the target person meets a second predetermined awareness condition according to the feature information and a number of times a third predetermined event occurs on the person information comprises:

determining that the target person meets a second preset popularity condition under the condition that the characteristic information comprises popularity values, the popularity values in the characteristic information are larger than a second preset value, and the frequency of occurrence of a third preset event on the person information is larger than a second preset frequency; otherwise, determining that the target person does not meet a second preset popularity condition.

10. A search result ranking apparatus, comprising:

the first acquisition module is used for searching and acquiring N enterprises based on the index information; wherein N is an integer greater than or equal to 2;

a determining module, configured to determine N ranking values corresponding to the N enterprises;

the screening module is used for screening M enterprises meeting a first preset popularity condition from the N enterprises; wherein M is an integer less than or equal to N;

the adjusting module is used for adjusting M ranking values corresponding to the M enterprises;

and the first sequencing module is used for sequencing the N enterprises according to the adjusted M sequencing values and the rest N-M sequencing values in the N sequencing values.

11. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the search result ranking method of any of the preceding claims 1 to 9 via execution of the executable instructions.

12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of ranking search results of any of the preceding claims 1 to 9.

13. A computer program comprising computer readable code for, when run on a device, a processor in the device executing instructions for carrying out the steps of the search result ranking method of any of claims 1 to 9.

Background

In a search scene, the ranking effect of a search result page is the most direct factor influencing the search experience of a user, and the quality of the ranking effect is closely related to the satisfaction degree of the user on products and the sustainable growth of the user quantity.

Currently, for a search scene, generally, a general ranking model is uniformly used to determine the ranking of search result pages, however, the general ranking model has great limitations in some cases, which may affect the ranking effect of the search result pages, thereby reducing the search experience of a user.

Disclosure of Invention

The invention aims to provide a search result sorting method, a search result sorting device and electronic equipment, which can improve the sorting effect of a search result page so as to ensure high-quality user search experience.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to a first aspect of the present disclosure, there is provided a search result ranking method, including:

searching and acquiring N enterprises based on the index information; wherein N is an integer greater than or equal to 2;

determining N ranking values corresponding to the N enterprises;

screening M enterprises meeting a first preset popularity condition from the N enterprises; wherein M is an integer less than or equal to N;

adjusting M ranking values corresponding to the M enterprises;

and sorting the N enterprises according to the adjusted M sorting values and the rest N-M sorting values in the N sorting values.

In an exemplary embodiment of the present disclosure, the adjusting the M ranking values corresponding to the M enterprises includes:

calculating the sum of a first preset value and a target ranking value corresponding to a target enterprise in the N ranking values; wherein the target enterprise is any one of the M enterprises;

and adjusting the target sorting value according to the sum value.

In an exemplary embodiment of the present disclosure, the adjusting the target ranking value according to the sum value includes:

calculating the sum of the additional value and the sum value;

updating the target rank value to the sum.

In an exemplary embodiment of the present disclosure, the index information includes target person name information;

the screening of M enterprises meeting a first preset popularity condition from the N enterprises includes:

acquiring a preset known enterprise set corresponding to the target name information;

and screening at least one enterprise in the set of known enterprises from the N enterprises to serve as M enterprises meeting a first preset awareness condition.

In an exemplary embodiment of the present disclosure, the obtaining of the preset known enterprise set corresponding to the target name information includes:

determining the number Q of target persons having the target person name information; wherein Q is an integer greater than or equal to 1;

In an exemplary embodiment of the present disclosure, the determining, according to the Q popularity values and at least part of preset Q related enterprise sets corresponding to Q target people, a preset known enterprise set corresponding to the target person name information includes:

merging preset associated enterprise sets corresponding to all the target characters in the character set to obtain a merged enterprise set;

carrying out duplicate removal processing on the merged enterprise set;

and determining the preset associated enterprise set corresponding to the screened target person as a preset known enterprise set corresponding to the target person name.

In an exemplary embodiment of the present disclosure, the index information includes target person name information;

the method further comprises the following steps:

acquiring feature information of a target person with the target person name information, and acquiring the frequency of a third preset event on the person information of the target person;

judging whether the target person meets a second preset popularity condition or not according to the feature information and the frequency of third preset events on the person information;

and under the condition that the target person does not meet the second preset popularity condition, sorting the N enterprises according to the N sorting values.

In an exemplary embodiment of the present disclosure, the determining whether the target person meets a second preset popularity condition according to the feature information and the number of times of occurrence of a third preset event on the person information includes:

determining that the target person meets a second preset popularity condition under the condition that the feature information comprises avatar information and popularity values, the popularity values in the feature information are larger than a second preset value, and the frequency of third preset events occurring on the person information is larger than a second preset frequency; otherwise, determining that the target person does not meet a second preset popularity condition.

According to a second aspect of the present disclosure, there is provided a search result ranking apparatus comprising:

the first acquisition module is used for searching and acquiring N enterprises based on the index information; wherein N is an integer greater than or equal to 2;

a determining module, configured to determine N ranking values corresponding to the N enterprises;

the screening module is used for screening M enterprises meeting a first preset popularity condition from the N enterprises; wherein M is an integer less than or equal to N;

the adjusting module is used for adjusting M ranking values corresponding to the M enterprises;

and the first sequencing module is used for sequencing the N enterprises according to the adjusted M sequencing values and the rest N-M sequencing values in the N sequencing values.

In an exemplary embodiment of the present disclosure, the adjusting module includes:

the calculation submodule is used for calculating the sum of a first preset value and a target ranking value corresponding to a target enterprise in the N ranking values; wherein the target enterprise is any one of the M enterprises;

and the adjusting submodule is used for adjusting the target sorting value according to the sum value.

In an exemplary embodiment of the present disclosure, the adjusting sub-module includes:

a first determining unit, configured to determine an additional value of the target enterprise according to the number of times of occurrence of a first preset event on the enterprise information of the target enterprise and the number of times of occurrence of the first preset event on the enterprise information of the target enterprise, which is shown based on the index information, before N enterprises are obtained by searching based on the index information;

a calculation unit for calculating a sum of the additional value and the sum value;

an updating unit for updating the target ranking value to the sum.

In an exemplary embodiment of the present disclosure, the index information includes target person name information;

the screening module includes:

the acquisition submodule is used for acquiring a preset known enterprise set corresponding to the target name information;

and the screening submodule is used for screening at least one enterprise in the known enterprise set from the N enterprises as M enterprises meeting a first preset popularity condition.

In an exemplary embodiment of the present disclosure, the obtaining sub-module includes:

a second determination unit configured to determine the number Q of target persons having the target person name information; wherein Q is an integer greater than or equal to 1;

the first processing unit is used for determining each enterprise, in a preset associated enterprise set corresponding to the target person, of which the frequency of second preset events occurring on enterprise information is greater than a first preset frequency, under the condition that Q is 1, and forming a preset known enterprise set corresponding to the target person name information by all the determined enterprises;

and the second processing unit is used for acquiring Q popularity values of Q target characters under the condition that Q is greater than 1, and determining a preset popularity enterprise set corresponding to the target character information according to the Q popularity values and at least part of preset related enterprise sets corresponding to the Q target characters.

In an exemplary embodiment of the present disclosure, the second processing unit includes:

a first filtering subunit, configured to filter, from the Q target persons, all the target persons having a highest awareness value up to an R-th height, if a difference between a awareness value of a first specific height and a awareness value of a second specific height of the Q awareness values is less than or equal to a preset difference; wherein R is an integer less than or equal to Q;

a first determining subunit, configured to determine a person set from all the screened target persons; the difference value of the awareness values of any two adjacent target people in the people set in height is smaller than or equal to the preset difference value;

a merging subunit, configured to merge preset associated enterprise sets corresponding to all the target people in the people set to obtain a merged enterprise set;

the first processing subunit is used for performing duplicate removal processing on the merged enterprise set;

and the second processing subunit is configured to determine each enterprise, in the merged enterprise set after the deduplication processing, for which the number of times that the second preset event occurs on the enterprise information is greater than the first preset number of times, and form a preset known enterprise set corresponding to the target name information by all the determined enterprises.

In an exemplary embodiment of the present disclosure, the second processing unit includes:

a second filtering subunit, configured to filter, from the Q target persons, the target person with the highest popularity value in a case where a difference between a first specific-height popularity value and a second specific-height popularity value among the Q popularity values is greater than a preset difference;

and the second determining subunit is configured to determine the preset associated enterprise set corresponding to the screened target person as a preset known enterprise set corresponding to the target person.

In an exemplary embodiment of the present disclosure, the index information includes target person name information;

the device further comprises:

the second acquisition module is used for acquiring the characteristic information of the target person with the target person name information and acquiring the frequency of third preset events on the person information of the target person;

the judging module is used for judging whether the target person meets a second preset popularity condition or not according to the feature information and the frequency of third preset events on the person information; under the condition that the target person meets the second preset popularity condition, triggering the screening module; under the condition that the target person does not meet the second preset popularity condition, triggering a second sorting module;

and the second sorting module is used for sorting the N enterprises according to the N sorting values.

In an exemplary embodiment of the present disclosure, the determining module is specifically configured to:

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above search result ranking method via execution of the executable instructions.

According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described search result ranking method.

According to a fifth aspect of the present disclosure, there is provided a computer program comprising computer readable code which, when run on an apparatus, a processor in the apparatus executes instructions of the steps of the above-mentioned search result ranking method.

As can be seen from the foregoing technical solutions, the search result ranking method, apparatus, electronic device, computer-readable storage medium, and computer program in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:

according to the search result sorting method in the embodiment of the disclosure, after N enterprises are obtained based on index information search and N sorting values corresponding to the N enterprises are determined, M enterprises meeting a first preset popularity condition can be screened from the N enterprises, the M sorting values are adjusted, and the N enterprises are sorted according to the adjusted M sorting values and the remaining N-M sorting values in the N sorting values. In the embodiment of the disclosure, the sequencing of the search result pages is not directly determined according to the N sequencing values obtained based on the universal sequencing model, but after the N sequencing values are obtained, the screening processing of the enterprises meeting the first preset popularity condition and the adjustment processing of the sequencing values corresponding to the screened enterprises are performed, so that the limitation of the universal sequencing model can be overcome, the rationality and the reliability of the sequencing values according to the sequencing determination are improved, the sequencing effect of the search result pages is improved, and the high-quality user search experience is ensured.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 is a schematic diagram of an exemplary system architecture to which embodiments of the present disclosure may be applied;

FIG. 2 is a flow chart of a search result ranking method in an exemplary embodiment of the present disclosure;

FIG. 3 is another flow chart of a search result ranking method in an exemplary embodiment of the present disclosure;

FIG. 4 is yet another flow chart of a search result ranking method in an exemplary embodiment of the present disclosure;

FIG. 5 is yet another flow chart of a search result ranking method in an exemplary embodiment of the present disclosure;

FIG. 6 is a block diagram of a search result ranking apparatus in an exemplary embodiment of the present disclosure;

FIG. 7 is another block diagram of a search result ranking apparatus in an exemplary embodiment of the present disclosure;

fig. 8 is a block diagram of an electronic device in an exemplary embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, apparatus, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. The symbol "/" generally indicates that the former and latter associated objects are in an "or" relationship.

In the present disclosure, unless otherwise expressly specified or limited, the terms "connected" and the like are to be construed broadly, e.g., as meaning electrically connected or in communication with each other; may be directly connected or indirectly connected through an intermediate. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.

As shown in fig. 1, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, server 105 may be a server cluster comprised of multiple servers, or the like.

The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may be various electronic devices having display screens including, but not limited to, smart phones, tablets, portable and desktop computers, digital cinema projectors, and the like.

The server 105 may be a server that provides various services. For example, a user sends a search request carrying index information to the server 105 by using the terminal device 103 (or the terminal device 101 or 102), and the server 105 may respond to the search request to obtain a search result and control the terminal device 103 to perform a presentation process of the search result on a search result page, so as to present the search result page to the user through the terminal device 103.

Referring to fig. 2, a flowchart of a search result ranking method according to an exemplary embodiment of the present disclosure is provided. The method shown in fig. 2 may include step 201, step 202, step 203, step 204 and step 205, which are described below.

Step 201, searching and acquiring N enterprises based on index information; wherein N is an integer greater than or equal to 2.

Here, N may be 2, 5, 8, 10, 16, 25, etc., and is not listed here.

The search scenario in the embodiment of the present disclosure may specifically be an enterprise search scenario, for example, a person name search enterprise scenario, so that in step 201, person name information (for example, target person name information hereinafter) may be input by a user as index information, and a search result including N enterprises may be obtained by performing a search using the index information.

Note that each of the N businesses has an association relationship with the name information as the index information, and for example, for any of the N businesses, the person having the name information is a corporate person, a high stock, an shareholder, or the like of the business.

Step 202, determine N ranking values corresponding to the N enterprises.

In step 202, N ranking values corresponding to the N enterprises may be determined based on the general ranking model; wherein, the N enterprises and the N ranking values can be in one-to-one correspondence.

Alternatively, the general ranking model may determine the ranking value with reference to relevance (e.g., text relevance) and/or authority, and the ranking value may be in a fractional form, and the general ranking model may determine the ranking value in a forward manner (e.g., the higher the relevance of the business to the index information, the higher the ranking value of the business), or in a reverse manner (e.g., the lower the relevance of the business to the index information, the higher the ranking value of the business).

Step 203, screening M enterprises meeting a first preset popularity condition from the N enterprises; wherein M is an integer less than or equal to N.

In step 203, the N enterprises may be traversed to screen out each of the N enterprises (which may also be referred to as a "famous enterprise"), and all the screened famous enterprises may be used as M enterprises meeting the first preset famous degree condition, or all the screened famous enterprises may be secondarily screened and all the secondarily screened famous enterprises may be used as M enterprises meeting the first preset famous degree condition.

And step 204, adjusting M ranking values corresponding to the M enterprises.

In step 204, M ranking values corresponding to the M enterprises screened in step 203 one by one may be determined from the N ranking values, and the M ranking values are adjusted.

Optionally, in a case that the general ranking model determines the ranking values in a forward manner, adjusting the M ranking values may include: carrying out numerical value promotion processing on the M sorting values; in the case where the general ranking model determines the ranking values in a reverse manner, adjusting the M ranking values may include: and carrying out numerical value reduction processing on the M sorting values.

And step 205, sorting the N enterprises according to the adjusted M sorting values and the rest N-M sorting values in the N sorting values.

It should be noted that, the adjusted M ranking values are in a one-to-one correspondence with M enterprises meeting the first preset popularity condition among the N enterprises, and the remaining N-M ranking values among the N ranking values are in a one-to-one correspondence with the remaining N-M enterprises except the M enterprises meeting the first preset popularity condition among the N enterprises, thus, the adjusted M ranking values can be used for determining the respective ranking positions of the M enterprises, the remaining N-M ranking values can be used for determining the respective ranking positions of the remaining N-M enterprises, the respective ranking positions of the N enterprises can be reasonably determined based on the adjusted M ranking values and the remaining N-M ranking values, therefore, N enterprises are reasonably sorted on the search result page, and the sorting effect of the search result page is further ensured.

Optionally, when the general ranking model determines the ranking values in a forward manner, the N enterprises may be displayed in an order of ranking values from top to bottom according to the adjusted M ranking values and the remaining N-M ranking values; under the condition that the general sorting model determines the sorting values in a reverse mode, the N enterprises can be displayed according to the sequence of the sorting values from low to high according to the adjusted M sorting values and the rest N-M sorting values.

According to the search result sorting method in the embodiment of the disclosure, after N enterprises are obtained based on index information search and N sorting values corresponding to the N enterprises are determined, M enterprises meeting a first preset popularity condition can be screened from the N enterprises, the M sorting values are adjusted, and the N enterprises are sorted according to the adjusted M sorting values and the remaining N-M sorting values in the N sorting values. In the embodiment of the disclosure, the sequencing of the search result pages is not directly determined according to the N sequencing values obtained based on the universal sequencing model, but after the N sequencing values are obtained, the screening processing of the enterprises meeting the first preset popularity condition and the adjustment processing of the sequencing values corresponding to the screened enterprises are performed, so that the limitation of the universal sequencing model can be overcome, the rationality and the reliability of the sequencing values according to the sequencing determination are improved, the sequencing effect of the search result pages is improved, and the high-quality user search experience is ensured.

Based on the embodiment shown in fig. 2, as shown in fig. 3, step 204 includes:

2041, calculating a sum of the first preset value and a target ranking value corresponding to the target enterprise in the N ranking values; wherein the target enterprise is any one of the M enterprises.

Here, the first preset value may be 10000, 15000, 20000, etc., and those skilled in the art can select the first preset value according to actual needs, which are not listed here.

And step 2042, adjusting the target sorting value according to the sum.

In one embodiment, step 2042 comprises:

determining the additional value of the target enterprise according to the frequency of the first preset event on the enterprise information of the target enterprise and the frequency of the first preset event on the enterprise information of the target enterprise displayed on the basis of the index information before N enterprises are searched and obtained on the basis of the index information;

calculating the sum of the added value and the sum value;

the target rank value is updated to a sum.

Generally, the search behavior of the user may be implemented by operating on an Application (APP), and the number of times of occurrence of the first preset event on the enterprise information of the target enterprise may be: in a first predetermined time period (for example, in the last half year, in the last quarter, etc.), the number of clicks of the enterprise information of the target enterprise on the APP (which may also be the number of shares, the number of collections, etc., and the case of the number of clicks is only described here as an example); before N enterprises are obtained based on index information search, the number of times of occurrence of a first preset event on the enterprise information of the target enterprise displayed based on the index information may be: in a first preset time period, due to the fact that the index information is used for searching, the click times of the enterprise information of the target enterprise on the APP are achieved under the condition that the enterprise information of the target enterprise is displayed on the APP.

It should be noted that, in the embodiments of the present disclosure, a plurality of predetermined time periods are involved, and specifically, a first predetermined time period in the above paragraph, and a second predetermined time period and a third predetermined time period in the following are included, and according to actual situations, any two of the first predetermined time period, the second predetermined time period, and the third predetermined time period may be the same or different.

In this embodiment, it is assumed that the number of times of occurrence of the first preset event on the enterprise information of the target enterprise is represented as DocClickCount, the number of times of occurrence of the first preset event on the enterprise information of the target enterprise shown based on the index information is represented as QuqryDocClickCount, the additional value is represented as score, and the additional value may be specifically calculated in the following manner:

when the QuqryDocClickCount > 0,

score = quqrydocclickcickcount 10+100 if DocClickCount > 100;

score = quqrydocclickchickcount 10+ DocClickCount if DocClickCount < ═ 100;

score = DocClickCount when QuqryDocClickCount ═ 0.

It is easy to see that, when the quqrydoclickcount and doccikcount are known, the added value of the target enterprise can be obtained efficiently by using the above calculation formula, and it should be noted that the parameter values such as "10" and "100" related to the above calculation formula can be adjusted according to actual conditions. By updating the target ranking score to the sum of the added value and the sum of the added value, the target ranking value can be increased by the sum of the first preset value and the added value on the basis of the original ranking value, so that the ranking position of the known enterprises on the search result page is ensured to be ahead, the ranking of the search result page is close to the perception of the user, the reasonable ranking position can be determined for each known enterprise through the difference of click data, and the ranking effect of the search result page is further improved.

Of course, the specific implementation of step 2042 is not limited to this, and for example, in step 2042, the target rank value may be directly updated to the sum value, which is also possible.

Therefore, in the embodiment of the disclosure, the target ranking value is adjusted according to the first preset value and the sum of the target ranking values corresponding to the target enterprises in the N ranking values, so that the ranking position of the known enterprises in the search result page is ensured to be ahead, and the ranking effect of the search result page is improved.

On the basis of the embodiment shown in fig. 2, the index information includes target person name information;

as shown in fig. 4, step 203 includes:

step 2031, a preset known enterprise set corresponding to the target name information is obtained.

Here, the correspondence relationship between the name and the known business set may be preset, so in step 2031, the preset known business set corresponding to the target name information may be determined according to the preset correspondence relationship. Of course, in step 2031, other manners may also be adopted to determine the known enterprise set corresponding to the target name information, and for clarity of layout, other manners are described in the following.

Step 2032, at least one enterprise in the set of known enterprises is screened from the N enterprises as M enterprises meeting the first preset awareness condition.

In step 2032, the N enterprises may be traversed to determine which of the N enterprises are located in the set of known enterprises corresponding to the target name information and which of the N enterprises are located outside the set of known enterprises corresponding to the target name information, so as to filter at least one enterprise located in the set of known enterprises corresponding to the target name information from the N enterprises (e.g., filter all enterprises located in the set of known enterprises corresponding to the target name information), so as to treat all the filtered enterprises as M enterprises meeting the first preset condition of popularity.

Therefore, in the embodiment of the disclosure, based on the known enterprise set corresponding to the target name information, M enterprises meeting the first preset popularity condition can be efficiently screened from the N enterprises.

On the basis of the embodiment shown in fig. 4, step 2031 includes:

determining the number Q of target persons with target person name information; wherein Q is an integer greater than or equal to 1;

under the condition that Q is 1, determining each enterprise, in a preset associated enterprise set corresponding to a target person, of which the frequency of second preset events on enterprise information is greater than the first preset frequency, and forming a preset known enterprise set corresponding to target person name information by all the determined enterprises;

and under the condition that Q is larger than 1, obtaining Q popularity values of Q target characters, and determining a preset popularity enterprise set corresponding to the name information of the target characters according to the Q popularity values and at least part of the preset related enterprise sets corresponding to the Q target characters.

Here, the number of times of occurrence of the second preset event on the business information of any one of the businesses may be: in a second predetermined time period (for example, in the last half year, in the last quarter, etc.), the number of clicks of the enterprise information of the enterprise on the APP (which may also be the number of shares, the number of collections, etc., and the case of the number of clicks is only described here as an example). The first preset number of times may be 5 times, 8 times, 10 times, 15 times, etc., and those skilled in the art can select the number according to actual needs, which are not listed here.

In the embodiment of the present disclosure, a database may be preset, where the database may store correspondence between characters and associated enterprise sets, where an associated enterprise set corresponding to any character includes a plurality of enterprises having an association with the character, for example, a plurality of enterprises including the character as a legal person, a high manager, a shareholder, and the like. In addition, the database may also store the corresponding relationship between the people and the popularity value.

In the case that the number Q of target persons having target person name information is 1, a related enterprise set corresponding to the target person information may be obtained from a database, then each enterprise having a click number greater than a first preset number may be determined from the obtained related enterprise set, and a known enterprise set corresponding to the target person name may be formed from all the determined enterprises.

In the case that the number Q of target persons having the target person name information is greater than 1, Q popularity values of the Q target persons may be obtained from the database, Q related enterprise sets corresponding to the Q target persons may be obtained from the database, and a known enterprise set corresponding to the target person name information may be determined according to the Q popularity values and at least a part of the Q related enterprise sets.

In a specific embodiment, determining a preset known enterprise set corresponding to target name information according to Q known values and at least part of preset Q associated enterprise sets corresponding to Q target characters includes:

screening all target people with the highest popularity values to the R-th highest from the Q target people under the condition that the difference value between the popularity value of the first specific height and the popularity value of the second specific height in the Q popularity values is smaller than or equal to a preset difference value; wherein R is an integer less than or equal to Q;

determining a person set from all the screened target persons; the difference value of the awareness values of any two target people in the people set, which are adjacent in height, is less than or equal to a preset difference value;

merging preset associated enterprise sets corresponding to all target characters in the character set to obtain a merged enterprise set;

carrying out de-duplication processing on the combined enterprise set;

and determining each enterprise of which the frequency of the second preset event occurring on the enterprise information is greater than the first preset frequency in the combined enterprise set after the duplication removal processing, and forming a preset known enterprise set corresponding to the target name information by all the determined enterprises.

Here, the first specific-height awareness value may refer to a highest awareness value among Q awareness values, and the second specific-height awareness value may refer to a second highest awareness value among the Q awareness values, but the awareness values of the first specific height and the second specific height are not limited thereto, for example, the first specific-height awareness value may refer to a second highest awareness value among the Q awareness values, and the second specific-height awareness value may refer to a third highest awareness value among the Q awareness values.

Here, the preset difference may be 100, 150, 200, etc., and those skilled in the art may select the preset difference according to actual needs, which are not listed here; r may be an integer less than or equal to Q, e.g., Q is 5 and R is 3.

Assuming that Q is 5 and R is 3, 5 popularity values may be arranged in an order from top to bottom, and a difference value of the popularity values sorted in the top two digits is determined, and if the determined difference value is less than or equal to a preset difference value, 3 target people with popularity values up to 3-th top may be screened from the 5 target people, and a people set may be determined from the screened 3 target people. Assuming that the 3 screened target persons are the target person 1, the target person 2 and the target person 3 respectively, and the awareness value of the target person 1 is the highest, and the awareness value of the target person 3 is the lowest, in the case that the difference between the awareness value of the target person 3 and the awareness value of the target person 2 is less than or equal to the preset difference, the awareness values of the target person 1, the target person 2 and the target person 3 can be considered to be at the same level, at this time, the target person 1, the target person 2 and the target person 3 can be included in the person set at the same time, and in the case that the difference between the awareness value of the target person 3 and the awareness value of the target person 2 is greater than the preset difference, the awareness values of the target person 1 and the target person 2 can be considered to be at the same level, and the awareness value of the target person 3 is far from the awareness values of the target person 1 and the target person 2, at this time, only the target person 1 and the target person 2 may be included in the person group. It is easy to see that the awareness of each target person in the person collection is at the same level.

Assuming that the person set includes 3 target persons, namely, target person 1, target person 2, and target person 3, the 3 associated enterprise sets corresponding to the 3 target persons may be merged to obtain a merged enterprise set.

And then, performing deduplication processing on the merged enterprise set to ensure that no repeated enterprises exist in the merged enterprise set after deduplication processing, and in addition, determining each enterprise with the click times larger than a first preset time from the merged enterprise set after deduplication processing, and forming a known enterprise set corresponding to the target name information by all the determined enterprises.

In this embodiment, in the case where there are a plurality of target persons with the same name, if the awareness degrees of two target persons with the highest awareness value and the second highest awareness value are at the same level, the known business set corresponding to the target person name information may be reasonably determined by combining the merging process of the associated business sets and the deduplication process of the merged business sets, and combining the click data.

In another specific embodiment, determining a preset known enterprise set corresponding to target person name information according to Q known value and at least part of preset Q associated enterprise sets corresponding to Q target persons includes:

screening a target person with the highest popularity value from the Q target persons under the condition that the difference value between the popularity value of the first specific height and the popularity value of the second specific height in the Q popularity values is larger than a preset difference value;

and determining the preset associated enterprise set corresponding to the screened target person as a preset known enterprise set corresponding to the target person name.

Here, the awareness values of the first specific height and the second specific height may refer to the awareness values described in the previous embodiment, and are not described herein again.

Here, the preset difference may be 100, 150, 200, etc., and those skilled in the art can select the preset difference according to actual needs, which are not listed here.

If Q is 4, 4 popularity values may be arranged in an order from top to bottom, and a difference value between the popularity values sorted in the top two digits is determined, and if the determined difference value is greater than a preset difference value, a target person with the highest popularity value may be screened from the 4 target persons, and the related enterprise set corresponding to the screened target person is directly determined as the public enterprise set corresponding to the target person name information.

In this embodiment, in the case where there are a plurality of target persons of the same name, if the awareness degrees of two target persons of which the awareness values are the highest and the second highest are not at the same level, the related business set corresponding to the most-known target person may be directly determined as the known business set corresponding to the target person name information.

Therefore, in the embodiment of the disclosure, no matter the number of the target persons is one or more than one, the known enterprise set corresponding to the target person name information can be determined in a proper manner, so that the reasonability and the reliability of the determined known enterprise set are ensured.

On the basis of the embodiment shown in fig. 2, the index information includes target person name information;

as shown in fig. 5, the method further includes:

step 211, acquiring feature information of the target person with the target person name information, and acquiring the number of times of a third preset event on the person information of the target person;

step 212, judging whether the target person meets a second preset popularity condition or not according to the frequency of the third preset event on the characteristic information and the person information; if the target person meets the second preset popularity condition, executing step 203; in the case that the target person does not satisfy the second preset popularity condition, executing step 213;

and step 213, sorting the N enterprises according to the N sorting values.

Here, steps 211 to 212 may be performed before steps 201 to 202, after steps 201 to 202, or concurrently with steps 201 to 202.

Here, the number of times of occurrence of the third preset event on any one of the personal information may be: in a third predetermined time period (for example, in the last half year, in the last quarter, etc.), the number of clicks of the personal information on the APP (which may also be the number of shares, the number of collections, etc., and the description is given only by taking the number of clicks as an example).

In the embodiment of the disclosure, the feature information of the target person with the target name information and the frequency of the third preset event occurring on the person information of the target person may be obtained from the server configured for the APP, and whether the target person meets the second preset naming degree condition is determined according to the feature information and the frequency of the third preset event occurring on the person information.

In one embodiment, the determining whether the target person meets the second preset popularity condition according to the feature information and the number of times of the third preset event occurring on the person information includes:

determining that the target person meets a second preset popularity condition under the condition that the characteristic information comprises avatar information and popularity values, the popularity values in the characteristic information are larger than a second preset value, and the frequency of third preset events occurring on the person information is larger than a second preset frequency; otherwise, determining that the target person does not meet the second preset popularity condition.

Here, the second preset value may be 4000, 5000, 6000, etc., and the second preset number may be 5, 8, 10, etc., which are not listed one by one.

In this embodiment, whether the target person meets the second preset popularity degree condition can be efficiently determined by combining whether the feature information includes the avatar information and the popularity value, the comparison result of the popularity value with the second preset value, and the comparison result of the number of times of the third preset event occurring on the person information with the second preset number.

Of course, the specific embodiment of determining whether the target person satisfies the second preset popularity condition is not limited thereto, and for example, the target person may be determined to satisfy the second preset popularity condition when the feature information includes avatar information and a popularity value and the popularity value in the feature information is greater than a second preset value.

In the case where the target person satisfies the second preset awareness condition, the target person may be considered to belong to the celebrity, and then, the above-described step 203 may be performed to screen M businesses satisfying the first preset awareness condition from among the N businesses, and further perform subsequent steps based on the screened M businesses.

Under the condition that the target person does not satisfy the second preset popularity condition, the target person can be considered not to belong to the celebrity, next, the N enterprises can be directly sorted according to the N sorting values, for example, the N enterprises can be directly displayed in the sequence of the sorting values from high to low.

In the embodiment of the disclosure, whether the target character is a celebrity or not can be identified by combining the characteristic information of the target character and the frequency of occurrence of a third preset event on the character information of the target character, and under the condition that the target character is the celebrity, screening processing of enterprises meeting a first preset popularity condition can be performed so as to adjust the ranking value of the celebrities in the N enterprises, so that the ranking effect of the search result page is ensured, and under the condition that the target character is not the celebrity, N enterprises are directly ranked and displayed according to the N ranking values, so that the search result page is efficiently presented to the user.

It should be noted that, in the current enterprise searching scenario, the enterprise name information may be used as the index information, or the name information (for example, the name of boss) may be used as the index information, when the name information is used as the index information, the correlations between the searched multiple enterprises and the name information are often in the same level, and when determining the ranking value for each of the searched multiple enterprises by using the universal ranking model and performing ranking display on the multiple enterprises based on the ranking value, it is difficult to ensure the ranking effect of the search result page.

In view of this, the overall design of the embodiment of the present disclosure may mainly include two parts, namely, the construction of the association relationship between the celebrity and the enterprise (knowledge construction) and the application of the knowledge in the ranking process (knowledge application). The knowledge construction is mainly based on the boss data, well-known bosses in the boss data are screened, then company entities related to the well-known bosses are mined, and famous companies in a company entity set are reserved to obtain a celebrity-celebrity association relation (corresponding to the process of acquiring a preset well-known enterprise set corresponding to the target name information in the foregoing). The knowledge application is to store the constructed knowledge set into an index, acquire knowledge corresponding to a search keyword in the index when a user sends a search request once (the constructed knowledge is acquired only when the search keyword is name information and a person with the name information belongs to a well-known boss), transmit the part of knowledge to a general sequencing model in the sequencing process, and the general sequencing model can promote the sequencing value of a famous enterprise in a recall result (for example, + 20000) to ensure that the sequencing position of the famous enterprise is relatively higher. The knowledge construction and knowledge application parts are specifically described below:

1. knowledge construction

Judging the celebrity: and judging whether the boss belongs to the famous boss or not by using boss data of a company as basic data according to the characteristics and the history of the boss clicked. The characteristics of the applied boss include whether the boss has a head portrait or not, and the boss popularity value (used for evaluating the popularity of the boss, the higher the value is, the more popular the boss is). Click data of boss comes from real click data statistics of users in the first half of APP. When a boss has a head portrait and the boss awareness value is higher than 5000 and the number of clicks of the boss is greater than 20, the boss is considered to be a celebrity (which is equivalent to the above-mentioned determination of whether the target character satisfies the second preset awareness condition based on the feature information and the number of times of occurrence of the third preset event on the character information).

(II) mining of company related to celebrities: and mining related companies by using the obtained celebrity data, reserving only known companies for the related companies of the celebrity, and forming an association relation between celebrity-known company entities, wherein the dimension of the mining relation between the person and the company comprises a legal person serving as the company, a stockholder serving as the company, a high-management serving as the company and the like. Among all companies associated with celebrities, the known company is judged to be the known company according to the number of clicks of the history of the company (the click data is from the real click data statistics of users in the first half of APP), and if and only if the history of the company is more than 5 clicks, the company is considered to be the known company. And obtaining the entity association relationship of the celebrity and the known company.

(III) merging and removing the weight of the celebrities: when a plurality of boss with the same name exist, whether the company entities associated with the plurality of boss with the same name need to be merged or only the company entity associated with one of the plurality of boss with the same name is reserved (duplication removal) needs to be confirmed according to the popularity distribution of the boss. Merging the same name boss: if the first three famous boss in the plurality of boss are in the same popularity (the difference of boss popularity values is less than 200), the company entities related to the first three famous boss can be merged to form a boss-known company list set (corresponding to the former concrete implementation mode of the preset famous enterprise set corresponding to the target name information according to the Q popularity values and at least part of the preset Q related enterprise sets corresponding to the Q target characters). Duplicate removal by the same name boss: when the popularity difference between the first two of the multiple best known employers is large (the popularity value difference between the employers exceeds 1000), only the company entity information associated with the best known employer is retained (corresponding to the latter specific implementation mode of determining the preset popular enterprise set corresponding to the target person name information according to the at least partial associated enterprise sets in the preset Q associated enterprise sets corresponding to the Q popularity values and the Q target persons).

2. Knowledge application

And storing the constructed celebrity-celebrity relationship entity into an index of an ES (the celebrity-celebrity relationship entity is called as an elastic search, and the elastic search is a search server), acquiring related knowledge stored in the ES according to a search keyword after a user triggers a search, acquiring company entity knowledge related to the keyword when the search word belongs to the name information and a person with the name information belongs to a known boss, and otherwise, acquiring a null result. In the sorting process, when the obtained result is not empty, the sorting value of the famous enterprise in the recall result is promoted according to the obtained result so as to ensure that the sorting position of the famous enterprise is earlier.

In summary, in the embodiments of the present disclosure, for a scene of searching for a person and looking up an enterprise, manually mined prior knowledge (i.e., a celebrity-celebrity association) may be introduced in the sorting process of a search result page to clarify a part belonging to a high-quality target result, so that the result of the part is weighted in the sorting.

Fig. 6 schematically shows a block diagram of a search result ranking apparatus according to an embodiment of the present disclosure. The search result ranking apparatus provided in the embodiment of the present disclosure may be disposed on a terminal device, may also be disposed on a server, or may be partially disposed on a terminal device and partially disposed on a server, for example, may be disposed on the server 105 in fig. 1 (according to actual replacement), but the present disclosure is not limited thereto.

The search result ranking apparatus provided by the embodiment of the present disclosure may include a first obtaining module 601, a determining module 602, a screening module 603, an adjusting module 604, and a first ranking module 605.

A first obtaining module 601, configured to obtain N enterprises based on index information search; wherein N is an integer greater than or equal to 2;

a determining module 602, configured to determine N ranking values corresponding to the N enterprises;

the screening module 603 is configured to screen M enterprises that meet a first preset popularity condition from the N enterprises; wherein M is an integer less than or equal to N;

an adjusting module 604, configured to adjust M ranking values corresponding to M enterprises;

the first ranking module 605 is configured to rank the N enterprises according to the adjusted M ranking values and the remaining N-M ranking values of the N ranking values.

In an alternative example, as shown in fig. 7, the adjusting module 604 includes:

a calculation submodule 6041, configured to calculate a sum of the first preset value and a target ranking value corresponding to the target enterprise in the N ranking values; wherein the target enterprise is any one of the M enterprises;

and an adjusting sub-module 6042 for adjusting the target sorting value according to the sum.

In an alternative example, the tuning sub-module 6042 includes:

the first determining unit is used for determining the additional value of the target enterprise according to the frequency of the first preset event on the enterprise information of the target enterprise and the frequency of the first preset event on the enterprise information of the target enterprise displayed on the basis of the index information before N enterprises are searched and obtained on the basis of the index information;

a calculation unit for calculating a sum of the added value and the sum value;

and the updating unit is used for updating the target sorting value into a sum.

In one optional example, the index information includes target person name information;

as shown in fig. 7, the screening module 603 includes:

an obtaining submodule 6031 configured to obtain a preset known enterprise set corresponding to the name information of the target person;

and a screening submodule 6032, configured to screen, from the N enterprises, at least one enterprise located in the set of known enterprises as M enterprises meeting a first preset awareness condition.

In an alternative example, the obtaining sub-module 6031 includes:

a second determination unit for determining the number Q of target persons having the target person name information; wherein Q is an integer greater than or equal to 1;

the first processing unit is used for determining each enterprise, in a preset associated enterprise set corresponding to a target person, of which the frequency of second preset events occurring on enterprise information is greater than the first preset frequency, under the condition that Q is 1, and forming a preset known enterprise set corresponding to target person name information by all the determined enterprises;

and the second processing unit is used for acquiring Q popularity values of Q target characters under the condition that Q is greater than 1, and determining a preset public business set corresponding to the target character name information according to the Q popularity values and at least part of preset associated business sets in the Q associated business sets corresponding to the Q target characters.

In one optional example, the second processing unit comprises:

a first filtering subunit, configured to filter, from the Q target persons, all target persons having a highest awareness value up to an R-th height, if a difference between the awareness value of the first specific height and the awareness value of the second specific height among the Q awareness values is less than or equal to a preset difference; wherein R is an integer less than or equal to Q;

a first determining subunit, configured to determine a person set from all the screened target persons; the difference value of the awareness values of any two target people in the people set, which are adjacent in height, is less than or equal to a preset difference value;

the merging subunit is used for merging preset associated enterprise sets corresponding to all target characters in the character set to obtain a merged enterprise set;

the first processing subunit is used for performing duplicate removal processing on the combined enterprise set;

and the second processing subunit is used for determining each enterprise in the merged enterprise set after the deduplication processing, wherein the times of the second preset events occurring on the enterprise information are greater than the first preset times, and all the determined enterprises form a preset known enterprise set corresponding to the target name information.

In one optional example, the second processing unit comprises:

a second filtering subunit, configured to filter, from the Q target persons, a target person with a highest popularity value in a case where a difference between a popularity value of the first specific height and a popularity value of the second specific height among the Q popularity values is greater than a preset difference;

and the second determining subunit is used for determining the preset associated enterprise set corresponding to the screened target person as a preset known enterprise set corresponding to the target person name.

In one optional example, the index information includes target person name information;

as shown in fig. 7, the apparatus further includes:

a second obtaining module 611, configured to obtain feature information of the target person having the target person name information, and obtain the number of times that a third preset event occurs on the person information of the target person;

the judging module 612 is configured to judge whether the target person meets a second preset popularity condition according to the feature information and the number of times of occurrence of a third preset event on the person information; under the condition that the target person meets a second preset popularity condition, triggering a screening module 603; triggering a second sorting module 613 under the condition that the target person does not meet a second preset popularity condition;

a second sorting module 613, configured to sort the N enterprises according to the N sorting values.

In an optional example, the determining module 612 is specifically configured to:

Alternatively, in order to determine whether the target person satisfies the second preset awareness condition, the awareness value in the feature information may be compared with a second preset value by using a comparator, and the number of times of occurrence of the third preset event on the person information may be compared with the second preset number of times by using the comparator.

According to the search result sorting device in the embodiment of the disclosure, after N sorting values are obtained, screening processing of enterprises meeting the first preset popularity condition and adjustment processing of the sorting values corresponding to the screened enterprises are performed, so that the limitation of a universal sorting model can be overcome, the reasonability and reliability of the sorting values according to the determination of sorting are improved, the sorting effect of search result pages is improved, and high-quality user search experience is guaranteed.

The specific implementation of each module, unit and subunit in the search result ranking apparatus provided in the embodiments of the present disclosure may refer to the content in the search result ranking method, and is not described herein again.

It should be noted that although several modules, units and sub-units of the apparatus for action execution are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules, units and sub-units described above may be embodied in one module, unit and sub-unit, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module, unit and sub-unit described above may be further divided into embodiments by a plurality of modules, units and sub-units.

As shown in FIG. 8, the example electronic device 80 includes a processor 801 for executing software routines although a single processor is shown for clarity, the electronic device 80 may include a multi-processor system. The processor 801 is connected to an infrastructure 802 for communicating with other components of the electronic device 80. The infrastructure 802 may include, for example, a communications bus, a crossbar, or a network.

Electronic device 80 also includes memory, such as Random Access Memory (RAM), which may include a main memory 803 and a secondary memory 810. Secondary memory 810 may include, for example, a hard disk drive 811 and/or a removable storage drive 812, which removable storage drive 812 may include a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive 812 reads from and/or writes to a removable storage unit 813 in a conventional manner. Removable storage unit 813 may comprise a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 812. As will be appreciated by one skilled in the relevant art, removable storage unit 813 includes a computer-readable storage medium having stored thereon computer-executable program code instructions and/or data.

In an alternative embodiment, secondary memory 810 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into electronic device 80. Such means may include, for example, a removable storage unit 821 and an interface 820. Examples of the removable storage unit 821 and the interface 820 include: a program cartridge (cartridge) and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 821 and interfaces 820 that allow software and data to be transferred from the removable storage unit 821 to electronic device 80.

The electronic device 80 also includes at least one communication interface 840. Communications interface 840 allows software and data to be transferred between electronic device 80 and external devices via communications path 841. In various embodiments of the present disclosure, communication interface 840 allows data to be transferred between electronic device 80 and a data communication network, such as a public or private data communication network. The communication interface 840 may be used to exchange data between different electronic devices 80, which electronic devices 80 form part of an interconnected computer network. Examples of communication interface 840 may include a modem, a network interface (such as an ethernet card), a communication port, an antenna with associated circuitry, and so forth. Communication interface 840 may be wired or may be wireless. Software and data transferred via communications interface 840 are in the form of signals which may be electrical, magnetic, optical or other signals capable of being received by communications interface 840. These signals are provided to a communications interface via communications path 841.

As shown in fig. 8, the electronic device 80 also includes a display interface 831 and an audio interface 832, the display interface 831 performing operations for rendering images to an associated display 830 and the audio interface 832 performing operations for playing audio content through an associated speaker 833.

In this document, the term "computer program product" may refer, in part, to: removable storage unit 813, removable storage unit 821, a hard disk installed in hard disk drive 811, or a carrier wave that carries software through communications path 841 (wireless link or cable) to communications interface 840. Computer-readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to electronic device 80 for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROMs, DVDs, Blu-ray (TM) disks, hard disk drives, ROMs, or integrated circuits, USB memory, magneto-optical disks, or a computer-readable card, such as a PCMCIA card, etc., whether internal or external to the electronic device 80. Transitory or non-tangible computer-readable transmission media may also participate in providing software, applications, instructions, and/or data to the electronic device 80, examples of such transmission media including radio or infrared transmission channels, network connections to another computer or another networked device, and the internet or intranet including e-mail transmissions and information recorded on websites and the like.

Computer programs (also called computer program code) are stored in the main memory 803 and/or the secondary memory 810. Computer programs may also be received via communications interface 840. Such computer programs, when executed, enable the electronic device 80 to perform one or more features of embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 801 to perform the features of the embodiments described above. Accordingly, such computer programs represent controllers of the computer system.

The software may be stored in a computer program product and loaded into the electronic device 80 using the removable storage drive 812, the hard disk drive 811, or the interface 820. Alternatively, the computer program product may be downloaded to computer system 80 via communications path 841. The software, when executed by the processor 801, causes the electronic device 80 to perform the functions of the embodiments described herein.

It should be understood that the embodiment of fig. 8 is given by way of example only. Accordingly, in some embodiments, one or more features of the electronic device 80 may be omitted. Also, in some embodiments, one or more features of the electronic device 80 may be combined together. Additionally, in some embodiments, one or more features of electronic device 80 may be separated into one or more components.

It will be appreciated that the elements shown in fig. 8 serve to provide a means for performing the various functions and operations of the server described in the above embodiments.

In one embodiment, a server may be generally described as a physical device including at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform necessary operations.

Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the functions of the method shown in fig. 2 to 5.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by an electronic device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.

An embodiment of the present application further provides a computer program, which includes a computer readable code, and when the computer readable code runs on a device, a processor in the device executes instructions of the steps in the above search result ranking method.

From the above description of the embodiments, it is clear to those skilled in the art that the embodiments of the present disclosure can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.

The basic principles of the present invention have been described above with reference to specific embodiments, but it should be noted that the advantages, effects, etc. mentioned in the present invention are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.

In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions.

The method and apparatus of the present invention may be implemented in a number of ways. For example, the methods and apparatus of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

完整详细技术资料下载

上一篇：石墨接头机器人自动装卡簧、装栓机

Search result ordering method and device and electronic equipment

相关技术

网友询问留言