应用于数字化社交的大数据话题推送方法及服务器

Big data topic pushing method and server applied to digital social contact

文档序号：7868 发布日期：2021-09-17 浏览：26次中文

1. A big data topic pushing method applied to digital social contact is characterized in that the big data topic pushing method is applied to a big data topic analysis server communicated with at least one digital social topic client, and the method comprises the following steps:

if the fact that the first interaction session theme tendency uploaded by at least one digital social topic client side is adjusted to be the second interaction session theme tendency is detected, determining adjustable theme tendency information based on the first interaction session theme tendency information and the second interaction session theme tendency information; the adjustable topic tendency information is used for expressing an interaction session topic tendency change record of the second interaction session topic tendency relative to the first interaction session topic tendency, the first interaction session topic tendency information comprises first interaction session topic tendency content corresponding to the at least one digital social topic client, and the second interaction session topic tendency information comprises second interaction session topic tendency content corresponding to the at least one digital social topic client;

based on the adjustable topic tendency information, adjusting a first topic grading and pushing mode to obtain a second topic grading and pushing mode, wherein the first topic grading and pushing mode is used for carrying out topic pushing optimization on the big data topic to be processed based on the first interactive session topic tendency, and the second topic grading and pushing mode is used for carrying out topic pushing optimization on the big data topic to be processed based on the second interactive session topic tendency;

and determining a large topic pushing optimization result of the large data topic to be processed based on the second interactive session topic tendency content and the second topic dividing and controlling pushing mode.

2. The method of claim 1, wherein determining adjustable topic tendency information based on the first interactive session topic tendency information and the second interactive session topic tendency information comprises:

determining a second topic tendency description based on the second interactive session topic tendency information, wherein the second topic tendency description is used for expressing the topic tendency pairing situation of the second interactive session topic tendency relative to the big data topic to be processed;

determining description update trend information between the second topic tendency description and a first topic tendency description to obtain an adjustable topic tendency description, wherein the first topic tendency description is used for expressing a topic tendency pairing condition of the first interaction session topic tendency relative to the to-be-processed big data topic;

and determining the adjustable theme tendency information according to the adjustable theme tendency description.

3. The method according to claim 1 or 2, wherein the adjusting the first topic segmentation and pushing manner based on the adjustable topic tendency information to obtain a second topic segmentation and pushing manner comprises:

summarizing the adjustable theme tendency information by using the information key words to obtain the theme tendency information corresponding to the description updating tendency information after the summary of the information key words;

adjusting the first topic dividing and pushing mode based on topic tendency information corresponding to the description updating tendency information after the information key vocabulary is summarized to obtain a second topic dividing and pushing mode;

the adjustable subject tendency information comprises adjustable subject tendency description, the adjustable subject tendency description refers to description updating tendency information between second subject tendency description and first subject tendency description, the first subject tendency description is used for expressing subject tendency pairing conditions of the first interactive session subject tendency relative to the big data topic to be processed, and the second subject tendency description is used for expressing subject tendency pairing conditions of the second interactive session subject tendency relative to the big data topic to be processed;

the information keyword summarization is performed on the adjustable topic tendency information to obtain topic tendency information corresponding to the description updating tendency information after the information keyword summarization, and the method comprises the following steps:

summarizing the adjustable theme tendency description by using the information key words to obtain a real-time theme tendency description corresponding to the description updating tendency information after the summary of the information key words;

the adjusting the first topic division and pushing mode based on the topic tendency information corresponding to the description updating tendency information after the information key vocabulary is summarized to obtain the second topic division and pushing mode comprises the following steps:

updating the topic push optimization sequence of the topic fragments in the first topic divide and conquer push mode according to the real-time topic tendency description corresponding to the description update tendency information after the information key vocabulary is summarized to obtain the second topic divide and conquer push mode.

4. The method of any one of claims 1 to 3, wherein determining a big topic push optimization result of the big data topic to be processed based on the second interactive session topic propensity content and the second topic subdivision push manner comprises:

acquiring a first time sequence corresponding relation between the first interactive conversation theme tendency content and an interactive conversation theme tendency corresponding to the current interactive conversation thread;

determining a second time sequence corresponding relation between first target tendency content and an interactive session theme tendency corresponding to a current interactive session thread based on the adjustable theme tendency information and session environment distribution information of the interactive session thread, wherein the first target tendency content refers to partial interactive session theme tendency content corresponding to the adjusted interactive session theme tendency in the second interactive session theme tendency content, and the adjusted interactive session theme tendency refers to partial interactive session theme tendency of which the second interactive session theme tendency is updated relative to the first interactive session theme tendency;

based on the first time sequence corresponding relation and the second time sequence corresponding relation, the second interactive conversation topic tendency content is transmitted into a topic pushing optimization model corresponding to the second topic grading and pushing mode, and a big topic pushing optimization result of the big data topic to be processed is obtained through the output of the topic pushing optimization model;

wherein the transmitting the second interactive session topic tendency content into the topic push optimization model corresponding to the second topic subdivision push manner based on the first time sequence corresponding relationship and the second time sequence corresponding relationship comprises:

based on the first time sequence corresponding relation, transmitting second target tendency content in the second interactive session theme tendency content into a topic push optimization model corresponding to the second topic divide-and-conquer push mode, wherein the second target tendency content refers to partial interactive session theme tendency content corresponding to an original interactive session theme tendency in the second interactive session theme tendency content, and the original interactive session theme tendency refers to partial interactive session theme tendency of which the second interactive session theme tendency does not have updating relative to the first interactive session theme tendency;

based on the second time sequence corresponding relation, the first target tendency content in the second interactive conversation theme tendency content is transmitted into a topic pushing optimization model corresponding to the second topic grading and pushing mode;

the obtaining of the large topic push optimization result of the big data topic to be processed through the topic push optimization model output comprises:

after the first target tendency content is transmitted into the topic pushing optimization model, transmitting the to-be-processed big data topic into the topic pushing optimization model;

calling a topic type classification unit in the topic pushing optimization model to perform topic type classification on the big data topic to be processed to obtain a topic type classification result corresponding to the big data topic to be processed;

determining a division topic pushing optimization mode aiming at the big data topic to be processed according to the topic type classification result; the topic dividing and pushing optimization mode comprises a project dividing and treating mode, a stream dividing and treating mode and a keyword dividing and treating mode;

based on the divided and treated topic pushing optimization mode, calling a corresponding topic pushing optimization unit in the topic pushing optimization model to perform topic pushing optimization on the big data topic to be processed to obtain a big topic pushing optimization result of the big data topic to be processed;

the method for optimizing topic pushing based on the topic division and management topic, calling a corresponding topic pushing optimization unit in the topic pushing optimization model to optimize topic pushing of the big data topic to be processed to obtain a big topic pushing optimization result of the big data topic to be processed, includes:

if the topic division and treatment pushing optimization mode is a project division and treatment mode, calling a project division and treatment unit in a topic pushing optimization model to determine a plurality of interactive conversation projects corresponding to the big data topics to be processed;

determining an interactive triggering condition according to a related interactive session item of a current interactive session item, wherein the related interactive session item is a time domain feature related interactive session item, a space domain feature related interactive session item or a multi-modal related interactive session item;

acquiring an integral interactive session item corresponding to the current interactive session item based on the interactive triggering condition;

acquiring an integral thread triggering condition of the integral interactive session project;

processing the current interactive session project based on the overall thread triggering condition to obtain a project operation record of the current interactive session project;

and carrying out topic pushing optimization on the big data topic to be processed according to the project operation record to obtain a current topic pushing optimization result, and outputting the current topic pushing optimization result.

5. The method of claim 4, wherein the step of determining an interactivity triggering condition based on an associated interactive session item of a current interactive session item comprises:

sequentially judging whether a prior interactive session keyword and a subsequent interactive session keyword of each airspace feature associated interactive session item of the current interactive session item are effective or not according to a set interactive session tendency judgment condition;

merging the topic activation derived information of all airspace feature associated interactive session items effective by the previous interactive session key words to obtain the topic activation derived information of the interactive trigger condition relative to the previous interactive session key words, and taking the interactive session item activation information of the airspace feature associated interactive session items effective by one of the previous interactive session key words as the interactive session item activation information of the interactive trigger condition relative to the previous interactive session key words;

and combining the topic activation derived information of all the airspace characteristic associated interactive session items effective by the subsequent interactive session key words to obtain the topic activation derived information of the interactive triggering condition relative to the subsequent interactive session key words, and taking the interactive session item activation information of the airspace characteristic associated interactive session items effective by one of the subsequent interactive session key words as the interactive session item activation information of the interactive triggering condition relative to the subsequent interactive session key words.

6. The method of claim 4, wherein the step of determining an interactivity triggering condition based on an associated interactive session item of a current interactive session item comprises:

sequentially judging whether a prior interactive session keyword and a subsequent interactive session keyword of each time domain feature associated interactive session item of the current interactive session item are valid according to a set interactive session tendency judgment condition;

merging the topic activation derived information of all time domain feature associated interactive session items effective by the previous interactive session key words to obtain topic activation derived information of the interactive trigger condition relative to the previous interactive session key words, and taking the interactive session item activation information of one time domain feature associated interactive session item effective by the previous interactive session key words as the interactive session item activation information of the interactive trigger condition relative to the previous interactive session key words;

and merging the topic activation derived information of all the time domain characteristic associated interactive session items effective by the subsequent interactive session key words to obtain the topic activation derived information of the interactive triggering condition relative to the subsequent interactive session key words, and taking the interactive session item activation information of one time domain characteristic associated interactive session item effective by the subsequent interactive session key words as the interactive session item activation information of the interactive triggering condition relative to the subsequent interactive session key words.

7. The method of claim 4, wherein the step of determining an interactivity triggering condition based on an associated interactive session item of a current interactive session item comprises:

sequentially judging whether the prior interactive session key words and the later interactive session key words of each multi-mode associated interactive session item of the current interactive session item are effective or not according to the set interactive session tendency judgment condition;

combining the topic activation derived information of all the multi-modal associated interactive session items with the effective previous interactive session keywords to obtain the topic activation derived information of the interactive triggering condition relative to the previous interactive session keywords, and taking the interactive session item activation information of the multi-modal associated interactive session items with the effective previous interactive session keywords as the interactive session item activation information of the interactive triggering condition relative to the previous interactive session keywords;

and merging the topic activation derived information of all the multi-modal associated interactive session items which are effective in the subsequent interactive session keywords to obtain the topic activation derived information of the interactive triggering condition relative to the subsequent interactive session keywords, and taking the interactive session item activation information of the multi-modal associated interactive session items which are effective in the subsequent interactive session keywords as the interactive session item activation information of the interactive triggering condition relative to the subsequent interactive session keywords.

8. The method according to claim 5, 6 or 7, wherein the step of obtaining the overall interactive session item corresponding to the current interactive session item based on the interactivity triggering condition comprises:

determining a to-be-determined interactive session item sequence according to the interactive session item activation information of the previous interactive session keyword and the interactive session item activation information of the next interactive session keyword;

and determining an integral interactive session item corresponding to the current interactive session item on the pending interactive session item sequence according to the topic activation derived information of the previous interactive session keyword and the topic activation derived information of the next interactive session keyword.

9. The method of claim 4, wherein the step of obtaining an integrity thread trigger condition for the integrity interaction session item comprises:

carrying out project time delay trimming based on the integral interactive session project to obtain a trimmed interactive session project;

respectively converting the overall interactive session project and the finished interactive session project into to-be-processed interactive session projects with a plurality of visual tendency category keywords;

acquiring an integral thread triggering condition of the interactive session item to be processed;

and comparing the number of the visual tendency category keywords of the overall thread triggering condition of the interactive session item to be processed to select the interactive session item to be processed with the smallest number of the visual tendency category keywords as a calibration interactive session item, and obtaining the overall thread triggering condition of the calibration interactive session item as the overall thread triggering condition of the overall interactive session item.

10. The big data topic analysis server is characterized by comprising a processing engine, a network module and a memory; the processing engine and the memory communicate through the network module, the processing engine reading a computer program from the memory and operating to perform the method of any of claims 1-9.

Background

With the continuous advance of digital transformation, the number and types of social networks are increasing, and new challenges are brought to the application of big data technology. When network information explodes, the scale of the network information is expanded sharply and has the characteristic of messy and unnecessary, and it is very important to extract valuable information through a big data mining technology and perform corresponding business processing by using the valuable information.

Big data topic push as one of big data application examples in the digital age, topics of interest or business services can be pushed to users. However, in the actual application process, how to improve the pertinence of topic push and reduce the influence on the topic push process is a technical problem which needs to be improved at present.

Disclosure of Invention

One embodiment of the application provides a big data topic pushing method applied to digital social contact, which is applied to a big data topic analysis server communicated with at least one digital social topic client, and the method comprises the following steps:

Preferably, the determining adjustable topic tendency information based on the first interactive session topic tendency information and the second interactive session topic tendency information includes:

and determining the adjustable theme tendency information according to the adjustable theme tendency description.

Preferably, the adjusting the first topic division and pushing mode based on the adjustable topic tendency information to obtain a second topic division and pushing mode includes:

Preferably, the determining a large topic push optimization result of the large data topic to be processed based on the second interactive session topic tendency content and the second topic grading and pushing manner includes:

the obtaining of the large topic push optimization result of the big data topic to be processed through the topic push optimization model output comprises:

after the first target tendency content is transmitted into the topic pushing optimization model, transmitting the to-be-processed big data topic into the topic pushing optimization model;

acquiring an integral interactive session item corresponding to the current interactive session item based on the interactive triggering condition;

acquiring an integral thread triggering condition of the integral interactive session project;

processing the current interactive session project based on the overall thread triggering condition to obtain a project operation record of the current interactive session project;

Preferably, the step of determining the interactivity triggering condition according to the associated interactive session item of the current interactive session item includes:

Preferably, the step of obtaining the overall interactive session item corresponding to the current interactive session item based on the interactivity triggering condition includes:

Preferably, the step of obtaining an integrity thread trigger condition of the integrity interaction session item includes:

carrying out project time delay trimming based on the integral interactive session project to obtain a trimmed interactive session project;

acquiring an integral thread triggering condition of the interactive session item to be processed;

One of the embodiments of the present application provides a big data topic analysis server, which includes a processing engine, a network module, and a memory; the processing engine and the memory communicate through the network module, and the processing engine reads the computer program from the memory and operates to perform the above-described method.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, in which like numerals indicate like structure, and in which:

FIG. 1 is a flow diagram illustrating an exemplary big data topic pushing method and/or process applied to digital social networking in accordance with some embodiments of the present invention;

FIG. 2 is a block diagram of an exemplary big data topic pushing device applied to digital social networking in accordance with some embodiments of the present invention;

FIG. 3 is a block diagram of an exemplary big data topic push system applied to digital social networking, shown in accordance with some embodiments of the present invention, an

FIG. 4 is a schematic diagram illustrating the hardware and software components in an exemplary big data topic analysis server in accordance with some embodiments of the present invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

In view of the problems described in the background art, the inventor provides a big data topic pushing method and a server applied to digital social contact in a targeted manner, and updates and changes of interactive conversation topic trends of a digital social topic client are analyzed to adaptively change a topic division pushing mode, so that topic pushing optimization is performed on a big data topic to be processed based on the latest topic division pushing mode, and thus, the obtained topic pushing optimization result can possibly meet the adjustable topic trend of a user.

First, a big data topic pushing method applied to digital social contact is exemplarily described, please refer to fig. 1, which is a flowchart illustrating an exemplary big data topic pushing method and/or process applied to digital social contact according to some embodiments of the present invention, and the big data topic pushing method applied to digital social contact may include the technical solutions described in the following steps S100 to S300.

S100: and if the fact that the first interaction session theme tendency uploaded by at least one digital social topic client is adjusted to be the second interaction session theme tendency is detected, determining adjustable theme tendency information based on the first interaction session theme tendency information and the second interaction session theme tendency information.

The digital social topic client in the embodiment of the application may be a digital intelligent terminal in communication with the big data topic analysis server, including but not limited to a mobile phone, a tablet computer, a desktop computer, a notebook computer, a wearable device, and the like. The big data topic analysis server can be an interactive session server, such as a cloud server. Further, the method can be applied to various fields, such as internet finance, block chain payment, cloud game service, intelligent traffic scheduling, intelligent factory management, cloud-side collaboration system, and the like, and is not limited herein. Further, the big data topic analysis server may communicate with a plurality of digital social topic clients at the same time, and only one digital social topic client is taken as an example for illustration.

It can be understood that the reporting time node of the first interactive session theme trend is earlier than that of the second interactive session theme trend, and the common understanding can be that the first interactive session theme trend is a historical interactive session theme trend, and the second interactive session theme trend is a latest interactive session theme trend. For example, the reporting time node time1 of the first interactive session theme trend may be 23 minutes and 40 seconds at 16 days 16 at 6 months and 14 days 2020, and the reporting time node time2 of the second interactive session theme trend may be 26 minutes and 50 seconds at 16 days 16 at 6 months and 14 days 2020, although the reporting time nodes of different interactive session theme trends may be other examples, which are not limited herein.

In an implementation of the present application, the adjustable topic tendency information is used to express an interaction session topic tendency change record of the second interaction session topic tendency relative to the first interaction session topic tendency, where the first interaction session topic tendency information includes first interaction session topic tendency content corresponding to the at least one digital social topic client, and the second interaction session topic tendency information includes second interaction session topic tendency content corresponding to the at least one digital social topic client. For example, the interactive session theme tendency change record of the first interactive session theme tendency may be: "the category of the topic preferred by the focused attention user is adjusted to the regional distribution of the topic preferred by the focused attention user".

It can be understood that the interactive session topic tendency information is obtained according to the interactive session topic tendency, for example, after the digital social topic client uploads the interactive session topic tendency, the big data topic analysis server can analyze and identify the corresponding interactive session topic tendency information according to the interactive session topic tendency. The interactive session theme tendency content is used for content-level distinguishing of the interactive session theme tendency, for example, the interactive session theme tendency content may be a "session content variation tendency" or a "session heat distribution tendency", which is not limited herein.

As can be seen from the above, in the era of big data and digitization, the interactive conversation topic tendency corresponding to the digitized social topic client is not fixed but has a dynamically variable characteristic, and therefore, in order to achieve flexibility of topic push optimization, it is relatively important to preferentially determine adjustable topic tendency information between different interactive conversation topic tendencies.

In this embodiment of the application, in order to ensure that the adjustable topic tendency information covers the significant interactive session tendency and the non-significant interactive session tendency of the user as much as possible, the determining the adjustable topic tendency information based on the first interactive session topic tendency information and the second interactive session topic tendency information may include the following information: determining a second topic tendency description based on the second interactive session topic tendency information, wherein the second topic tendency description is used for expressing the topic tendency pairing situation of the second interactive session topic tendency relative to the big data topic to be processed; determining description update trend information between the second topic tendency description and a first topic tendency description to obtain an adjustable topic tendency description, wherein the first topic tendency description is used for expressing a topic tendency pairing condition of the first interaction session topic tendency relative to the to-be-processed big data topic; and determining the adjustable theme tendency information according to the adjustable theme tendency description.

For example, the topic tendency description is used for summarizing the topic tendency information of the interactive session, and the topic tendency pairing situation of the second interactive session topic relative to the topic tendency topic of the big data topic to be processed can be understood as a matching situation between the topic tendency description and the big data topic to be processed, for example, the topic tendency description is "cosmetics", and the big data topic to be processed is mainly for "vehicles", so the topic tendency pairing situation can represent low matching between the topic tendency description and the big data topic to be processed. For another example, if the topic tendency description is "fitness" and the big data topic to be processed is mainly for "protein powder", then the topic tendency matching condition can represent high matching between the topic tendency description and the big data topic to be processed.

For example, the description update tendency information may be expressed in a form of a knowledge graph, so that all situations of description changes of the user may be analyzed as much as possible, and meanwhile, non-significant interaction session tendencies may be mined based on an association relationship between graph nodes and edge attributes in the knowledge graph, so that when the adjustable topic tendency information is determined according to the adjustable topic tendency description, the corresponding non-significant interaction session tendencies may be described by analyzing the adjustable topic tendency, and it is ensured that the adjustable topic tendency information covers the significant interaction session tendencies and the non-significant interaction session tendencies of the user as much as possible.

For some embodiments that can be implemented independently, before determining the adjustable topic tendency information based on the first interactive session topic tendency information and the second interactive session topic tendency information, the method further includes: acquiring first interaction session theme tendency information corresponding to a first interaction session theme tendency uploaded by at least one digital social topic client; generating a first conversation topic dividing and pushing mode based on the first interactive conversation topic tendency information; and determining a first time sequence corresponding relation between the first interactive session theme tendency content and the interactive session theme tendency corresponding to the current interactive session thread based on the first interactive session theme tendency information and the session environment distribution information of the interactive session thread.

S200: and adjusting the first topic grading and pushing mode based on the adjustable topic tendency information to obtain a second topic grading and pushing mode.

In the embodiment of the application, the first topic grading and pushing mode is used for optimizing topic pushing on the big data topic to be processed based on the first interactive session topic tendency, and the second topic grading and pushing mode is used for optimizing topic pushing on the big data topic to be processed based on the second interactive session topic tendency. It can be understood that the first topic division pushing mode and the second topic division pushing mode are different, and the second topic division pushing mode can be obtained after adjustment and adjustment are carried out on the basis of the first topic division pushing mode. Furthermore, the topic dividing and pushing mode may correspond to a machine learning model, and is used for processing the topic to be transmitted as the to-be-processed big data topic to output the topic pushing optimization result in the visual aspect, and the machine learning model may be obtained by pre-training, and the training process may refer to the content of the existing relevant model, which is not described herein again. It can be understood that the topic divide-and-conquer push mode in the embodiment of the application is not fixed, but is adjusted in real time according to the adjustable topic tendency information, so that the topic push optimization result can be ensured to be matched with the latest user interactive conversation topic tendency as much as possible, and the flexibility and efficiency of topic push optimization are improved in a digital social environment.

In this embodiment of the application, in order to ensure that the second topic division and pushing manner can satisfy the interactive conversation topic tendency of the user as much as possible, the adjusting the first topic division and pushing manner based on the adjustable topic tendency information to obtain the second topic division and pushing manner may include S210 and S220.

S210: and summarizing the adjustable theme tendency information by using the information key words to obtain the theme tendency information corresponding to the description updating tendency information after the summary of the information key words.

For example, the information keywords are used for summarizing and distinguishing different theme tendency information, and the keywords with deviations can be optimized through summarizing the information keywords, so that the one-to-one correspondence between the information keywords and the theme tendency information is ensured.

It can be understood that the adjustable subject tendency information includes adjustable subject tendency description, the adjustable subject tendency description refers to description update tendency information between second subject tendency description and first subject tendency description, the first subject tendency description is used for expressing subject tendency pairing situation of the first interactive session subject tendency relative to the big data topic to be processed, and the second subject tendency description is used for expressing subject tendency pairing situation of the second interactive session subject tendency relative to the big data topic to be processed. Based on this, the summarizing the adjustable topic tendency information by the information keyword to obtain the topic tendency information corresponding to the description update tendency information after summarizing the information keyword vocabulary may include: and summarizing the adjustable theme tendency description by using the information key words to obtain the real-time theme tendency description corresponding to the description updating tendency information after the summary of the information key words.

S220: and adjusting the first topic dividing and pushing mode based on the topic tendency information corresponding to the description updating tendency information after the information key vocabulary is summarized to obtain a second topic dividing and pushing mode.

Further, after the topic tendency information corresponding to the description update tendency information after the information key vocabulary is determined based on S210, the first topic grading and pushing manner is adjusted based on the topic tendency information corresponding to the description update tendency information after the information key vocabulary is determined, and the manner adjustment can be performed, or the current topic tendency of the user is considered as much as possible in the network parameter adjustment process, so that it can be ensured that the second topic grading and pushing manner can satisfy the interactive conversation topic tendency of the user as much as possible. On the basis of the implementation mode corresponding to S210, the adjusting the first topic division and pushing manner based on the topic tendency information corresponding to the description update tendency information after the information key vocabulary is summarized to obtain the second topic division and pushing manner includes: updating the topic push optimization sequence of the topic fragments in the first topic divide and conquer push mode according to the real-time topic tendency description corresponding to the description update tendency information after the information key vocabulary is summarized to obtain the second topic divide and conquer push mode. For example, the topic push optimization sequence of the topic fragments can be understood as a topic push optimization sequence for the big data topic fragments or the conversation topic sets, and descriptions about the big data topic fragments or the conversation topic sets are explained below and are not described herein again.

It can be understood that the main purpose of S200 is to implement adjustment of the second topic division and pushing manner, so as to ensure that the latest interactive session topic tendency of the user can be considered as much as possible when subsequently performing topic push optimization, and in addition, because the adjustment of the second topic division and pushing manner is performed based on the first topic division and pushing manner, the parameter scale of the machine learning model corresponding to the adjusted second topic division and pushing manner does not increase, that is, when performing topic push optimization by using the second topic division and pushing manner, the adjustment is implemented based on the original session time interval constraint and the session scene constraint, so that normal topic push optimization can be ensured.

S300: and determining a large topic pushing optimization result of the large data topic to be processed based on the second interactive session topic tendency content and the second topic dividing and controlling pushing mode.

In the embodiment of the application, the related technical layers comprise interactive conversation theme tendency content, a topic division and push mode and the big data topics to be processed, and the corresponding visual layer optimization needs to be considered for processing the big data topics to be processed, so that the efficiency of topic push optimization can be improved by adopting a machine learning model (a topic push optimization model), and compared with the topic push optimization modes of functional programming and declarative programming, the resource overhead of a big data topic analysis server can be reduced by adopting an artificial intelligence model, and the time sequence of topic push optimization is improved. To achieve this, the determining a big topic push optimization result of the big data topic to be processed based on the second interactive session topic tendency content and the second topic divide and conquer push manner may include S310-S330.

S310: and acquiring a first time sequence corresponding relation between the first interactive conversation theme tendency content and the interactive conversation theme tendency corresponding to the current interactive conversation thread.

The current interactive session thread in the embodiment of the present application may be understood as an execution thread that outputs interactive session information. The interactive conversation theme tendency corresponding to the current interactive conversation thread can be understood as the voice output tendency of the voice output thread, and the time sequence corresponding relation is used for representing a time sequence difference comparison result between the interactive conversation tendency of the user side and the interactive conversation tendency of the interactive side. Generally, the timing of the interactive session tendency of the interactive side is weaker than that of the user side.

S320: and determining a second time sequence corresponding relation between first target tendency content and the interactive session theme tendency corresponding to the current interactive session thread based on the adjustable theme tendency information and the session environment distribution information of the interactive session thread, wherein the first target tendency content refers to partial interactive session theme tendency content corresponding to the adjusted interactive session theme tendency in the second interactive session theme tendency content, and the adjusted interactive session theme tendency refers to partial interactive session theme tendency of which the second interactive session theme tendency is updated relative to the first interactive session theme tendency.

In the embodiment of the present application, the session environment distribution information is used to represent corresponding information respectively output by different session states of the interactive session thread, for example, the interactive session thread may be divided into a session state a, a session state B, a session state C, and a session state D, and the session environment distribution information may cover spatial information and session information amount respectively corresponding to the session state a, the session state B, the session state C, and the session state D.

S330: and based on the first time sequence corresponding relation and the second time sequence corresponding relation, transmitting the second interactive conversation topic tendency content into a topic pushing optimization model corresponding to the second topic grading and pushing mode, and outputting a large topic pushing optimization result of the large data topic to be processed through the topic pushing optimization model. Wherein the transmitting the second interactive session topic tendency content into the topic push optimization model corresponding to the second topic subdivision push manner based on the first time sequence corresponding relationship and the second time sequence corresponding relationship comprises: based on the first time sequence corresponding relation, transmitting second target tendency content in the second interactive session theme tendency content into a topic push optimization model corresponding to the second topic divide-and-conquer push mode, wherein the second target tendency content refers to partial interactive session theme tendency content corresponding to an original interactive session theme tendency in the second interactive session theme tendency content, and the original interactive session theme tendency refers to partial interactive session theme tendency of which the second interactive session theme tendency does not have updating relative to the first interactive session theme tendency; and based on the second time sequence corresponding relation, transmitting the first target tendency content in the second interactive conversation theme tendency content into a topic pushing optimization model corresponding to the second topic grading and pushing mode.

In the embodiment of the application, by analyzing the corresponding relations of different time sequences, the difference of the time sequences of the interactive sessions between the user side and the interaction side can be considered as much as possible when the topic pushing optimization model corresponding to the second topic grading and pushing mode is used for transmitting the topic tendency content of the second interactive session, so that the switching time delay is reduced as much as possible when the topic pushing optimization model is used for outputting the large topic pushing optimization result of the large data topic to be processed.

It can be understood that S310-S330 describe the preamble steps of performing big data topic interactive sessions based on the topic push optimization model, that is, the timing difference between the user side and the interactive side is considered, so that the flexibility of interactive session output can be improved and the switching delay of the interactive session result can be reduced on the premise of ensuring the size of the original interactive session thread.

Further, in S330, the obtaining of the large topic push optimization result of the to-be-processed large data topic through the topic push optimization model output may include S3300: after the second target tendency content is transmitted into the topic push optimization model, transmitting the to-be-processed big data topic into the topic push optimization model; calling a topic type classification unit in the topic pushing optimization model to perform topic type classification on the big data topic to be processed to obtain a topic type classification result corresponding to the big data topic to be processed; determining a division topic pushing optimization mode aiming at the big data topic to be processed according to the topic type classification result; the topic dividing and pushing optimization mode comprises a project dividing and treating mode, a stream dividing and treating mode and a keyword dividing and treating mode; and calling a corresponding topic pushing optimization unit in the topic pushing optimization model to perform topic pushing optimization on the big data topic to be processed based on the dividing and treating topic pushing optimization mode to obtain a big topic pushing optimization result of the big data topic to be processed.

In the embodiment of the present application, the S3300 focuses on topic push optimization of the big data topic to be processed, and the topic category of the big data topic to be processed may be understood as a topic field corresponding to the big data topic to be processed, such as an online commerce field, a cloud game service field, or a cosmetic production field, which is not limited herein. Further, the topic-based push optimization method may include three different methods, which will be described below.

Firstly, introducing a project division and management mode, in S3300, calling a corresponding topic push optimization unit in the topic push optimization model to perform topic push optimization on the big data topic to be processed based on the division and management topic push optimization mode, so as to obtain a big topic push optimization result of the big data topic to be processed, which may include S3310-S3360.

S3310: and if the topic division and treatment pushing optimization mode is a project division and treatment mode, calling a project division and treatment unit in a topic pushing optimization model to determine a plurality of interactive conversation projects corresponding to the big data topics to be processed.

S3320: and determining an interactive triggering condition according to an associated interactive session item of the current interactive session item, wherein the associated interactive session item is a time domain feature associated interactive session item, a space domain feature associated interactive session item or a multi-modal associated interactive session item. In the implementation of the application, the interactive triggering condition is used for representing various kinds of constraint or restrictive information corresponding to the conversion of abstract topic data into graphical data, the time domain feature association can be understood as association in a time sequence, the space domain feature association can be understood as association in an execution thread running state, and the multi-modal association is combination of association in the time sequence and association in the running state.

In the embodiment of the application, the accurate determination of the interactive triggering condition is a key for ensuring the accuracy and reliability of topic push optimization, and therefore, the interactive triggering condition can be determined based on different types of associated interactive session projects. Here, determining the interactivity triggering condition according to the associated interactive session item of the current interactive session item may be implemented by the following three cases.

In a first case, the step of determining an interactivity triggering condition according to an associated interactive session item of a current interactive session item includes: sequentially judging whether a prior interactive session keyword and a subsequent interactive session keyword of each airspace feature associated interactive session item of the current interactive session item are effective or not according to a set interactive session tendency judgment condition; merging the topic activation derived information of all airspace feature associated interactive session items effective by the previous interactive session key words to obtain the topic activation derived information of the interactive trigger condition relative to the previous interactive session key words, and taking the interactive session item activation information of the airspace feature associated interactive session items effective by one of the previous interactive session key words as the interactive session item activation information of the interactive trigger condition relative to the previous interactive session key words; and combining the topic activation derived information of all the airspace characteristic associated interactive session items effective by the subsequent interactive session key words to obtain the topic activation derived information of the interactive triggering condition relative to the subsequent interactive session key words, and taking the interactive session item activation information of the airspace characteristic associated interactive session items effective by one of the subsequent interactive session key words as the interactive session item activation information of the interactive triggering condition relative to the subsequent interactive session key words.

In a second case, the step of determining an interactivity triggering condition according to an associated interactive session item of a current interactive session item includes: sequentially judging whether a prior interactive session keyword and a subsequent interactive session keyword of each time domain feature associated interactive session item of the current interactive session item are valid according to a set interactive session tendency judgment condition; merging the topic activation derived information of all time domain feature associated interactive session items effective by the previous interactive session key words to obtain topic activation derived information of the interactive trigger condition relative to the previous interactive session key words, and taking the interactive session item activation information of one time domain feature associated interactive session item effective by the previous interactive session key words as the interactive session item activation information of the interactive trigger condition relative to the previous interactive session key words; and merging the topic activation derived information of all the time domain characteristic associated interactive session items effective by the subsequent interactive session key words to obtain the topic activation derived information of the interactive triggering condition relative to the subsequent interactive session key words, and taking the interactive session item activation information of one time domain characteristic associated interactive session item effective by the subsequent interactive session key words as the interactive session item activation information of the interactive triggering condition relative to the subsequent interactive session key words.

In a third case, the step of determining an interactivity triggering condition according to an associated interactive session item of a current interactive session item includes: sequentially judging whether the prior interactive session key words and the later interactive session key words of each multi-mode associated interactive session item of the current interactive session item are effective or not according to the set interactive session tendency judgment condition; combining the topic activation derived information of all the multi-modal associated interactive session items with the effective previous interactive session keywords to obtain the topic activation derived information of the interactive triggering condition relative to the previous interactive session keywords, and taking the interactive session item activation information of the multi-modal associated interactive session items with the effective previous interactive session keywords as the interactive session item activation information of the interactive triggering condition relative to the previous interactive session keywords; and merging the topic activation derived information of all the multi-modal associated interactive session items which are effective in the subsequent interactive session keywords to obtain the topic activation derived information of the interactive triggering condition relative to the subsequent interactive session keywords, and taking the interactive session item activation information of the multi-modal associated interactive session items which are effective in the subsequent interactive session keywords as the interactive session item activation information of the interactive triggering condition relative to the subsequent interactive session keywords.

S3330: and acquiring an overall interactive session item corresponding to the current interactive session item based on the interactive triggering condition.

In this embodiment of the application, the overall interactive session item may be an item that is important to the whole big data topic interactive session process, for example, some core topic public sentiments interactive session items, and based on this, the step of obtaining the overall interactive session item corresponding to the current interactive session item based on the interactivity triggering condition includes: determining a to-be-determined interactive session item sequence according to the interactive session item activation information of the previous interactive session keyword and the interactive session item activation information of the next interactive session keyword; and determining an integral interactive session item corresponding to the current interactive session item on the pending interactive session item sequence according to the topic activation derived information of the previous interactive session keyword and the topic activation derived information of the next interactive session keyword. In the embodiment of the application, the undetermined interactive session item sequence comprises a plurality of interactive session items, and the topic activation derived information can be understood as flow information aiming at topic information visual transformation.

S3340: and acquiring an integral thread triggering condition of the integral interaction session item. In an embodiment of the present application, the step of obtaining the overall thread trigger condition of the overall interactive session item includes: carrying out project time delay trimming based on the integral interactive session project to obtain a trimmed interactive session project; respectively converting the overall interactive session project and the finished interactive session project into to-be-processed interactive session projects with a plurality of visual tendency category keywords; acquiring an integral thread triggering condition of the interactive session item to be processed; and comparing the number of the visual tendency category keywords of the overall thread triggering condition of the interactive session item to be processed to select the interactive session item to be processed with the smallest number of the visual tendency category keywords as a calibration interactive session item, and obtaining the overall thread triggering condition of the calibration interactive session item as the overall thread triggering condition of the overall interactive session item.

S3350: and processing the current interactive session project based on the overall thread triggering condition to obtain a project running record of the current interactive session project. In the embodiment of the application, the project operation record may include visual form selection and visual form adjustment instructions related to the big data topic to be processed, such as which topic information is converted into table content and which topic information is converted into a thermodynamic diagram.

S3360: and carrying out topic pushing optimization on the big data topic to be processed according to the project operation record to obtain a current topic pushing optimization result, and outputting the current topic pushing optimization result. In the embodiment of the application, the push optimization result of the current topic can be output to the corresponding interaction side for displaying.

In the embodiment of the invention, in addition to the above-mentioned topic pushing and optimizing by a project division and treatment method, topic pushing and optimizing can be performed based on stream division and keyword division, further, based on the division and treatment topic pushing and optimizing method in the above-mentioned information, a corresponding topic pushing and optimizing unit in the topic pushing and optimizing model is called to perform topic pushing and optimizing on the big data topic to be processed, so as to obtain a big topic pushing and optimizing result of the big data topic to be processed, and the following two implementation methods can be used to implement the following two implementation methods.

Embodiment mode 1

If the dividing and controlling topic pushing optimization mode is a streaming dividing and controlling mode, calling a streaming dividing and controlling network in a topic pushing optimization model to determine a plurality of big data topic fragments corresponding to the big data topic to be processed; and determining a hierarchical processing instruction according to the big data topic fragments, performing hierarchical processing on the big data topic fragments according to the hierarchical processing instruction to obtain a target topic push optimization result, and outputting the target interactive conversation result uninterruptedly.

In embodiment 1, the stream-based subdivision adopts stream-based reading of topic fragments, an interactive session process is divided into a plurality of stages, the big data topic analysis server performs hierarchical acceleration processing processes, a hierarchical processing instruction can be used for guiding processing priorities of different big data topic fragments, and an obtained target topic push optimization result is adjustable and changeable when the interactive session is output. For example, according to the hierarchical processing instruction, the topic push optimization of the big data topic segment topic1 is preferentially performed to obtain a line graph, then the line graph is displayed, then after a period of time, the topic push optimization result (thermodynamic diagram) of the big data topic segment topic2 is displayed, after a period of time, the topic push optimization result (adjustable table content) of the big data topic segment topic3 is displayed, and the previously output line graph is deleted at the same time. By the design, the interactive session result can be ensured to be in accordance with the actual tendency of the user as much as possible on the premise of limited time and limited session scene. For example, if the user wants to know the result of the interactive session corresponding to the big data topic segment topic1, the line graph, the thermodynamic diagram, the adjustable table content, and so on may be displayed first.

Embodiment mode 2

If the dividing and addressing topic pushing optimization mode is a keyword dividing and addressing mode, calling a keyword dividing and addressing network in a topic pushing optimization model to determine a plurality of conversation topic sets corresponding to the big data topics to be processed; and carrying out topic push optimization by taking the conversation topic set as granularity and outputting a topic push optimization result.

In embodiment 2, keyword ranking is a single-program multiple-data method, and the conversation topic set in embodiment 2 and the big-data topic piece in embodiment 1 are different. The session topic set in the embodiment 2 may be clustered by a clustering algorithm, for example, a K-means clustering algorithm, and the big data topic segments are obtained by splitting the big data topic to be processed. For another example, if the big data to be processed is topic { topic1, topic2, topic3, topic4, topic5, topic6 }. The big data topic snippets may be: big data topic segment topic1, big data topic segment topic2, big data topic segment topic3, big data topic segment topic4, big data topic segment topic5, and big data topic segment topic 6. The set of conversation topics may be { topic1, topic3}, { topic5}, and { topic2, topic4, topic6 }. By means of the design, processing is performed in a keyword dividing and treating mode, so that the interactive session result can be ensured to be in accordance with the actual tendency of the user as much as possible on the premise that the time is limited and the session scene is limited, and the data relevance among big data topics can be taken into account, so that the pertinence of the push result is improved, and the richness of topic contents contained in the push result is further increased.

In conclusion, by implementing the technical scheme, the interactive conversation topic tendency uploaded by the digital social topic client can be detected in real time, so that the topic division and pushing manner can be adaptively changed by analyzing the update change of the interactive conversation topic tendency of the digital social topic client, and the topic push optimization is performed on the big data topic to be processed based on the latest topic division and pushing manner, so that the obtained topic push optimization result can possibly meet the adjustable topic tendency of the user. In addition, topic push optimization is carried out through different divide and conquer push optimization modes, the pertinence of push results can be improved, and the richness of topic contents contained in the push results is further increased.

Next, for the above big data topic pushing method applied to the digital social network, an exemplary big data topic pushing device applied to the digital social network is further provided in the embodiment of the present invention, as shown in fig. 2, the big data topic pushing device 200 applied to the digital social network may include the following functional modules.

The tendency determining module 210 is configured to determine adjustable subject tendency information based on the first interaction session subject tendency information and the second interaction session subject tendency information if it is detected that the first interaction session subject tendency uploaded by the at least one digital social topic client is adjusted to the second interaction session subject tendency; the adjustable topic tendency information is used for expressing a visual topic tendency change record of the second interactive session topic tendency relative to the first interactive session topic tendency, the first interactive session topic tendency information comprises first interactive session topic tendency content corresponding to the at least one digital social topic client, and the second interactive session topic tendency information comprises second interactive session topic tendency content corresponding to the at least one digital social topic client.

The mode adjusting module 220 is configured to adjust a first topic grading and pushing mode based on the adjustable topic tendency information to obtain a second topic grading and pushing mode, where the first topic grading and pushing mode is used to perform topic pushing and optimization on the big data topic to be processed based on the first interactive session topic tendency, and the second topic grading and pushing mode is used to perform topic pushing and optimization on the big data topic to be processed based on the second interactive session topic tendency.

The pushing optimization module 230 is configured to determine a large topic pushing optimization result of the large data topic to be processed based on the second interactive session topic tendency content and the second topic treatment pushing manner.

For the description of the functional modules shown in fig. 2, please refer to the description of the method shown in fig. 1, which is not repeated herein.

Then, based on the above method embodiment and apparatus embodiment, the embodiment of the present invention further provides a system embodiment, that is, a big data topic pushing system applied to digital social contact, please refer to fig. 3 in combination, where the big data topic pushing system 30 applied to digital social contact may include a big data topic analysis server 10 and a digital social topic client 20. Wherein the big data topic analysis server 10 and the digital social topic client 20 are in communication to implement the above method, and further, the functionality of the big data topic pushing system 30 applied to digital social networking is described as follows.

A big data topic pushing system applied to digital social contact comprises a big data topic analysis server and at least one digital social topic client which are communicated with each other;

the at least one digital social topic client is used for uploading interactive session topic trends to the big data topic analysis server;

the big data topic analysis server is used for:

For further description of the system shown in fig. 3, please refer to the description of the method shown in fig. 1, which is not repeated herein.

Further, referring to fig. 4 in conjunction, the big data topic analysis server 10 may include a processing engine 110, a network module 120, and a memory 130, the processing engine 110 and the memory 130 communicating through the network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.

It is to be understood that the configuration shown in fig. 4 is merely illustrative, and the big data topic analysis server 10 may also include more or fewer components than shown in fig. 4, or have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.

The foregoing disclosure of embodiments of the present invention will be apparent to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

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Big data topic pushing method and server applied to digital social contact

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