1. A commodity recommendation method based on user value is characterized by comprising the following steps:

acquiring consumption attributes of a current user;

acquiring consumption data of the current user, and acquiring consumption data of other users with the same consumption attributes as the current user;

performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain a user value type of the current user;

and recommending the target commodity to the current user according to the user value type.

2. The user value-based commodity recommendation method according to claim 1, wherein the obtaining of the consumption attribute of the current user comprises:

acquiring a user portrait of a current user;

and determining the consumption attribute of the user according to the user portrait and based on a preset mapping relation between the user portrait and the consumption attribute.

3. The user value-based commodity recommendation method according to claim 1, wherein if the target commodity is a virtual commodity, the obtaining of the consumption attribute of the current user comprises:

acquiring the game duration data of the current user and browsing data of various virtual commodities of the mall;

and determining the consumption attribute of the current user according to the game duration data and the browsing data.

4. The user value-based commodity recommendation method according to claim 1, wherein the performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user comprises:

based on an RFM model, carrying out value analysis on the consumption data of the current user and the consumption data of other users to obtain the data value degree of each consumption data;

and carrying out statistical analysis on the data value degree of each consumption data to obtain the user value type of the current user.

5. The user value-based commodity recommendation method according to claim 1, wherein the performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user comprises:

based on an RFM model, carrying out value analysis on the consumption data of the current user to obtain the data value degree of each consumption data of the current user;

based on an RFM model, performing value analysis on the consumption data of other users to obtain the standard data value degree of each consumption data;

comparing the data value degree of each consumption data of the current user with the standard data value degree of each consumption data, and judging the data value degree of the consumption data with the comparison result of less than the standard data value degree as the non-abnormal data value degree;

and carrying out statistical analysis on the non-abnormal data value degree of each consumption data to obtain the user value type of the current user.

6. The user value-based commodity recommendation method according to claim 1, wherein said recommending a target commodity to the current user according to the user value type comprises:

acquiring a corresponding commodity to be recommended according to the user value type and based on a preset mapping relation between the user value type and the recommended commodity;

selecting the first N commodities in all the commodities to be recommended as target commodities; n is an integer and is greater than or equal to 1;

and recommending the target commodity to the current user.

7. A commodity recommendation device based on a user value, comprising:

the first acquisition module is used for acquiring the consumption attribute of the current user;

the second acquisition module is used for acquiring the consumption data of the current user and acquiring the consumption data of other users with the same consumption attributes as the current user;

the statistical analysis module is used for performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user;

and the commodity recommending module is used for recommending the target commodity to the current user according to the user value type.

8. The user value-based commodity recommendation device according to claim 7, wherein the commodity recommendation module specifically comprises:

the to-be-recommended commodity obtaining unit is used for obtaining a corresponding to-be-recommended commodity according to the user value type and based on a preset mapping relation between the user value type and the recommended commodity;

the target commodity selecting unit is used for selecting the first N commodities in all the commodities to be recommended as target commodities; n is an integer and is greater than or equal to 1;

and the target commodity recommending unit is used for recommending the target commodity to the current user.

9. A commodity recommendation apparatus based on a user value, comprising:

a memory for storing a computer program;

a processor for executing the computer program;

wherein the processor implements the user value-based commodity recommendation method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium storing a computer program which, when executed, implements the user value-based commodity recommendation method according to any one of claims 1 to 6.

Background

The prior art generally performs corresponding commodity recommendations for a user based on the value classification of the user (e.g., performs corresponding virtual commodity recommendations for a game user based on the value classification of the game user). However, when the user value is classified, if the consumption data of the user is small (that is, if the user is a new user), it is difficult to perform reasonable user value analysis on the user, and the obtained user value classification result is not accurate enough, which results in low accuracy of recommending the product and poor user experience.

Disclosure of Invention

The technical problem to be solved by the embodiment of the invention is as follows: the commodity recommendation method, the commodity recommendation device, the commodity recommendation equipment and the storage medium based on the user value are provided, the commodity recommendation accuracy is improved, and further the user experience is improved.

In order to solve the above technical problem, in a first aspect, an embodiment of the present invention provides a commodity recommendation method based on a user value, where the commodity recommendation method includes:

acquiring consumption attributes of a current user;

acquiring consumption data of the current user, and acquiring consumption data of other users with the same consumption attributes as the current user;

performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain a user value type of the current user;

and recommending the target commodity to the current user according to the user value type.

As a preferred scheme, the acquiring the consumption attribute of the current user includes:

acquiring a user portrait of a current user;

and determining the consumption attribute of the user according to the user portrait and based on a preset mapping relation between the user portrait and the consumption attribute.

As a preferred scheme, if the target product is a virtual product, the obtaining of the consumption attribute of the current user includes:

acquiring the game duration data of the current user and browsing data of various virtual commodities of the mall;

and determining the consumption attribute of the current user according to the game duration data and the browsing data.

As a preferred scheme, the performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user includes:

based on an RFM model, carrying out value analysis on the consumption data of the current user and the consumption data of other users to obtain the data value degree of each consumption data;

and carrying out statistical analysis on the data value degree of each consumption data to obtain the user value type of the current user.

As a preferred scheme, the performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user includes:

based on an RFM model, carrying out value analysis on the consumption data of the current user to obtain the data value degree of each consumption data of the current user;

based on an RFM model, performing value analysis on the consumption data of other users to obtain the standard data value degree of each consumption data;

comparing the data value degree of each consumption data of the current user with the standard data value degree of each consumption data, and judging the data value degree of the consumption data with the comparison result of less than the standard data value degree as the non-abnormal data value degree;

and carrying out statistical analysis on the non-abnormal data value degree of each consumption data to obtain the user value type of the current user.

As a preferred scheme, the recommending a target commodity to the current user according to the user value type includes:

acquiring a corresponding commodity to be recommended according to the user value type and based on a preset mapping relation between the user value type and the recommended commodity;

selecting the first N commodities in all the commodities to be recommended as target commodities; n is an integer and is greater than or equal to 1;

and recommending the target commodity to the current user.

In order to solve the above technical problem, in a second aspect, an embodiment of the present invention provides a commodity recommendation device based on a user value, where the commodity recommendation device includes:

the first acquisition module is used for acquiring the consumption attribute of the current user;

the second acquisition module is used for acquiring the consumption data of the current user and acquiring the consumption data of other users with the same consumption attributes as the current user;

the statistical analysis module is used for performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user;

and the commodity recommending module is used for recommending the target commodity to the current user according to the user value type.

As a preferred scheme, the commodity recommendation module specifically includes:

the to-be-recommended commodity obtaining unit is used for obtaining a corresponding to-be-recommended commodity according to the user value type and based on a preset mapping relation between the user value type and the recommended commodity;

the target commodity selecting unit is used for selecting the first N commodities in all the commodities to be recommended as target commodities; n is an integer and is greater than or equal to 1;

and the target commodity recommending unit is used for recommending the target commodity to the current user.

In order to solve the above technical problem, in a third aspect, an embodiment of the present invention provides a commodity recommendation device based on a user value, where the commodity recommendation device includes:

a memory for storing a computer program;

a processor for executing the computer program;

wherein the processor, when executing the computer program, implements the user value-based item recommendation method according to any one of the first aspect.

In order to solve the above technical problem, in a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, which when executed, implements the user value-based item recommendation method according to any one of the first aspect.

Compared with the prior art, the commodity recommendation method, the commodity recommendation device, the commodity recommendation equipment and the storage medium based on the user value have the advantages that: based on the consumption attributes of the users, the consumption data of the user and the consumption data of other users with the same consumption attributes are obtained, then the consumption data of the user and the consumption data of other users with the same consumption attributes are subjected to statistical analysis, so that the user value type of the user is determined, commodity recommendation is carried out according to the user value type of the user, the user value type classification is carried out on the user needing commodity recommendation and the consumption data of other users with the same consumption attributes, the commodity recommendation accuracy rate can be improved under the condition that the consumption data of the user are few, and the user experience is further improved.

Drawings

In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is apparent that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on the drawings without inventive labor.

FIG. 1 is a flow chart diagram of a preferred embodiment of a commodity recommendation method based on user value according to the present invention;

FIG. 2 is a schematic structural diagram of a preferred embodiment of a commodity recommendation device based on user value according to the present invention;

fig. 3 is a schematic structural diagram of a preferred embodiment of a commodity recommendation device based on user value according to the present invention.

Detailed Description

In order to clearly understand the technical features, objects and effects of the present invention, the following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention, but are not intended to limit the scope of the invention. Other embodiments, which can be derived by those skilled in the art from the embodiments of the present invention without inventive step, shall fall within the scope of the present invention.

In the description of the present invention, it should be understood that the numbers themselves, such as "first", "second", etc., are used only for distinguishing the described objects, do not have a sequential or technical meaning, and cannot be understood as defining or implying the importance of the described objects.

Fig. 1 is a flowchart illustrating a commodity recommendation method based on user value according to a preferred embodiment of the present invention.

As shown in fig. 1, the commodity recommendation method includes the steps of:

s10: acquiring consumption attributes of a current user;

s20: acquiring consumption data of the current user, and acquiring consumption data of other users with the same consumption attributes as the current user;

s30: performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain a user value type of the current user;

s40: and recommending the target commodity to the current user according to the user value type.

Wherein the consumption attribute of the user is used for indicating the consumption characteristics of the user, and may include consumption level levels, such as a high consumption level, a medium consumption level, a low consumption level, and the like; the consumption attribute of the user may also include consumption preference, for example, if the user is a game user, the consumption attribute of the user may be preference for exchanging skin consumption, preference for enhancing prop consumption, etc.

Specifically, in the case that the consumption data of the user is less, the invention firstly obtains the consumption attribute of the user needing commodity recommendation, wherein the consumption attribute can be determined by the user representation of the current user or can be determined by the browsing data of the current user on various commodities in the mall. After the consumption attribute of the current user is obtained, the consumption data of the user is obtained, and the consumption data of other users with the same consumption attribute as the user is obtained, wherein the number of the other users is not limited, when the number is large, the accuracy of the result can be improved to be higher, and when the number is small, the calculation time can be shortened. And then, carrying out statistical analysis on the consumption data of the current user and the consumption data of other users through a pre-established user value analysis model so as to determine the user value type of the current user. And finally, acquiring the corresponding target commodity according to the user value type of the current user, and recommending the target commodity to the user.

According to the commodity recommendation method based on the user value, the consumption data of the user and the consumption data of other users with the same consumption attributes are obtained based on the consumption attributes of the user, then the consumption data of the user and the consumption data of other users with the same consumption attributes are subjected to statistical analysis, the user value type of the user is determined, commodity recommendation is performed according to the user value type of the user, the user value type classification is performed by integrating the consumption data of the user needing commodity recommendation and the consumption data of other users with the same consumption attributes, the commodity recommendation accuracy can be improved under the condition that the consumption data of the user are few, and further user experience is improved.

In a preferred embodiment, the obtaining of the consumption attribute of the current user includes:

acquiring a user portrait of a current user;

and determining the consumption attribute of the current user according to the user portrait and based on a preset mapping relation between the user portrait and the consumption attribute.

Specifically, the embodiment first obtains the user portrait, obtains an analysis result according to the analysis dimension of the user portrait and the value of each analysis dimension, and then determines the consumption attribute of the user based on a preset mapping relationship according to the analysis result.

As an example, a user representation has eight analysis dimensions: p stands for basic (Primary): whether the user representation is based on a contextual interview of a real user; e represents homology (Empathy): the user portrait contains description related to names, photos and commodities, and whether the user portrait has the same sense of mind or not; r stands for authenticity (Realistic): whether the user portrait looks like a real person or not; s stands for uniqueness (singleplex): whether each user is unique, with little similarity to each other; o stands for objective (Objectives): whether the user representation contains a high-level target related to the commodity or not and whether the user representation contains a keyword to describe the target or not; n represents numerical (Number): whether the number of user representations is sufficiently small so that the design team can remember the name of each user representation, and one of the primary user representations; a stands for applicability (Applicable): whether a design team can make a design decision using the user representation as a utility tool; l represents permanence (Long): longevity of the user tag. After the user portrait is obtained, the value of each analysis dimension is obtained, and then an analysis result is calculated according to a preset analysis formula:wherein R is the analysis result, omega_iIs the weight of the ith analysis dimension, and xi is the value of the ith analysis dimension. And finally, determining the consumption attribute of the user based on the mapping relation between the analysis result and the consumption attribute: and T ═ f (r), where T is the consumption attribute, and f is the mapping relationship between the analysis result and the consumption attribute.

The acquiring of the user portrait of the current user specifically includes:

establishing a historical user portrait of the current user according to the historical feature tag of the current user;

establishing a temporary user portrait of the current user according to an inherent feature tag in the historical feature tags; wherein the intrinsic characteristic label comprises at least one of an age label and a gender label;

acquiring other feature labels except the inherent feature label of the current user within preset time;

acquiring the matching degree of other feature labels except the inherent feature label of the current user in preset time and other feature labels except the inherent feature label in the historical feature labels;

if the matching degree is larger than a preset matching degree threshold value, taking the historical user portrait as the user portrait of the current user;

if the matching degree is not larger than a preset matching degree threshold value, establishing a latest user portrait of the current user according to other feature tags except the inherent feature tag of the current user in a preset time and the inherent feature tag of the current user, and taking the latest user portrait as the user portrait of the current user.

When acquiring a user portrait, the present embodiment creates a user portrait including an inherent tag, a behavior tag, and the like in advance from previously accumulated feature tag data. The inherent labels comprise inherent attributes of the users such as the gender, the age and the like of the users, and can be collected from the users in links such as user registration; the behavior tags comprise the behaviors of interactive operations of adding concern, canceling concern, adding a wish list, taking out the wish list, forming an order, canceling the order, paying, refunding and the like of the user, and are acquired after the authorization of the user. When a user is newly on line, a temporary user portrait is established for the user, and inherits the inherent label from the historical user portrait to reflect the inherent attributes of the user such as gender, age and the like; and the feature tag reflecting the user behavior in the temporary user portrait is obtained by acquiring the behavior in the preset time, and the matching degree of the newly acquired feature tag data and the previously accumulated feature tag data is judged. When the matching degree is larger than a preset matching degree threshold value, the latest behavior of the user is higher in consistency with the historical user portrait, so that the historical user portrait can be used as the user portrait; and when the matching degree is not greater than the preset matching degree threshold value, which indicates that the latest behavior of the user deviates from the historical user portrait, establishing the latest user portrait again according to the inherent label and the newly acquired feature label, and replacing the historical user portrait with the latest user portrait to serve as the user portrait.

The embodiment can realize effective maintenance and updating of the user portrait according to the latest behavior data of the user, particularly under the condition that the user behavior changes suddenly, the latest user portrait of the user can be established quickly by using the temporary user portrait and the latest behavior preference of the user, and the latest user portrait can reflect the latest preference of the user more accurately, so that the consumption attribute of the user is determined more accurately, and the accuracy of commodity recommendation is improved.

In a preferred embodiment, if the target product is a virtual product, the obtaining of the consumption attribute of the current user includes:

acquiring the game duration data of the current user and browsing data of various virtual commodities of the mall;

and determining the consumption attribute of the current user according to the game duration data and the browsing data.

In the embodiment, the consumption attribute of the user is directly determined by the game duration data and the browsing data of the user on various virtual commodities in the shopping mall, so that the consumption attribute of the user can be quickly determined.

As an example, there is a new piece of fashion (or called skin) in the mall, the time of online is 54min after the user logs in the game, in 54min, it takes 22min to browse the goods on sale in the mall, and the browsing time reaches 13 mm and the number of times of browsing reaches 6 times for fashion A. At this time, it is indicated that the user has a purchasing desire for the goods in the mall, and the purchasing desire for the fashion a is strong, it can be obtained that the consumption attribute of the user is: has strong consumption desire, and the consumption preference is fashion A.

In a preferred embodiment, the performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user includes:

based on an RFM model, carrying out value analysis on the consumption data of the current user and the consumption data of other users to obtain the data value degree of each consumption data;

and carrying out statistical analysis on the data value degree of each consumption data to obtain the user value type of the current user.

It should be noted that the RFM model is an important tool and means for measuring the user value and the user profit-making ability, and is composed of three elements: recent consumption (Recency), Frequency of consumption (Frequency), and amount of consumption (money).

In the embodiment, under the condition that the consumption data of the user is less, the consumption data of the user is acquired, the consumption data of other users belonging to the same consumption attribute with the user is acquired, two kinds of consumption data are comprehensively analyzed, the data value degree of each kind of consumption data is obtained, the user value type of the user is further determined, and the defect that the consumption data of the user is less can be overcome by using the consumption data of other users with the same consumption attribute.

In a preferred embodiment, the performing statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain the user value type of the current user includes:

based on an RFM model, carrying out value analysis on the consumption data of the current user to obtain the data value degree of each consumption data of the current user;

based on an RFM model, performing value analysis on the consumption data of other users to obtain the standard data value degree of each consumption data;

comparing the data value degree of each consumption data of the current user with the standard data value degree of each consumption data, and judging the data value degree of the consumption data with the comparison result of less than the standard data value degree as the non-abnormal data value degree;

and carrying out statistical analysis on the non-abnormal data value degree of each consumption data to obtain the user value type of the current user.

As an example, after performing value analysis on other users with the same consumption attribute, the standard data value degree of the consumption frequency obtained by the value analysis is 4.5 times/day, and the data value degree of the consumption frequency of the current user obtained by the value analysis is 16 times/day, at this time, the two data value degrees are compared to obtain a value of 16 > 4.5, which indicates that the data value degree of the current user under the consumption frequency is abnormal, and needs to be discarded to avoid misjudgment.

In this embodiment, value analysis is performed on consumption data of a current user and consumption data of other users belonging to the same consumption attribute as the user through an RFM model, so as to obtain a standard data value of each consumption data and a data value of each consumption data of the current user, and then a data value determined to be non-abnormal is obtained through comparison, so as to determine a user value type of the user.

Optionally, when the data value degrees are compared, the data value degrees within the preset range of the standard data value degrees may be used as non-abnormal data value degrees, and the data value degrees outside the preset range may be used as abnormal data value degrees.

As an example, after value analysis is performed on other users with the same consumption attribute, the standard data value degree of the consumption frequency obtained is 4.5 times/day, and the data value degree of the consumption frequency obtained by analysis is 5 times/day, at this time, the two are compared to obtain the value of 5E [4.5-0.6, 4.5+0.6 ]]If so, the data value degree of the current user under the consumption frequency is not abnormal; if the data value degree of the consumption frequency of the current user is obtained by analysis and is 6 times/day, the data value degree and the consumption frequency are compared to obtainExplain the present usageThe data value degree of the user under the consumption frequency is abnormal and needs to be abandoned.

Furthermore, after the abnormal data value degree is obtained, the abnormal data value degree can be corrected through the standard data value degree.

Wherein, the correction process can directly replace the abnormal data value degree with the standard data value.

In a preferred embodiment, the recommending a target commodity to the current user according to the user value type includes:

acquiring a corresponding commodity to be recommended according to the user value type and based on a preset mapping relation between the user value type and the recommended commodity;

selecting the first N commodities in all the commodities to be recommended as target commodities; n is an integer and is greater than or equal to 1;

and recommending the target commodity to the current user.

Specifically, in the preset mapping relationship, a specific user value type has a plurality of corresponding to-be-recommended commodities, each to-be-recommended commodity has a recommendation appropriateness, and a higher recommendation appropriateness indicates a higher probability of fitting the preference of the user, and a lower recommendation appropriateness indicates a lower probability of fitting the preference of the user. At this time, N commodities with the highest recommendation appropriateness need to be selected from all the commodities to be recommended as target commodities to be recommended to the user, wherein the value of N may be preset or may be modified by the user.

It should be understood that all or part of the processes in the above commodity recommendation method based on user value may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a processor, to implement the steps of the above commodity recommendation method based on user value. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.

Fig. 2 is a schematic structural diagram of a preferred embodiment of a commodity recommendation device based on user value according to the present invention, which is capable of implementing all processes of the commodity recommendation method based on user value according to any one of the above embodiments and achieving corresponding technical effects.

As shown in fig. 2, the article recommendation apparatus includes:

a first obtaining module 21, configured to obtain a consumption attribute of a current user;

a second obtaining module 22, configured to obtain consumption data of the current user, and obtain consumption data of other users having the same consumption attribute as the current user;

the statistical analysis module 23 is configured to perform statistical analysis on the consumption data of the current user and the consumption data of the other users to obtain a user value type of the current user;

and the commodity recommending module 24 is used for recommending the target commodity to the current user according to the user value type.

Wherein the consumption attributes of the user may include consumption level levels, such as a high consumption level, a medium consumption level, a low consumption level, and the like; consumption preferences may also be included, such as preference for fashion consumption, preference for enhanced prop consumption, preference for good prop consumption, and the like.

In a preferred embodiment, the first obtaining module 21 specifically includes:

a user portrait acquisition unit for acquiring a user portrait of a current user;

and the first consumption attribute determining unit is used for determining the consumption attribute of the user according to the user portrait and based on the preset mapping relation between the user portrait and the consumption attribute.

In a preferred embodiment, the user representation obtaining unit specifically includes:

the historical user portrait establishing subunit is used for establishing a historical user portrait of the current user according to the historical feature tag of the current user;

the temporary user portrait establishing subunit is used for establishing a temporary user portrait of the current user according to the inherent feature tag in the historical feature tags; wherein the intrinsic characteristic label comprises at least one of an age label and a gender label;

the characteristic label acquiring subunit is used for acquiring other characteristic labels of the current user in a preset time except the inherent characteristic label;

the characteristic label matching subunit is used for acquiring the matching degree of other characteristic labels of the current user in a preset time except the inherent characteristic label and other characteristic labels of the historical characteristic labels except the inherent characteristic label;

a first user portrait determining subunit, configured to, if the matching degree is greater than a preset matching degree threshold, use the historical user portrait as a user portrait of the current user;

and the second user portrait determining subunit is used for establishing the latest user portrait of the current user according to other feature tags except the inherent feature tag of the current user within a preset time and the inherent feature tag of the current user if the matching degree is not greater than a preset matching degree threshold value, and taking the latest user portrait as the user portrait of the current user.

In a preferred embodiment, if the target product is a virtual product, the first obtaining module 21 specifically includes:

the user data acquisition unit is used for acquiring the game duration data of the current user and browsing data of various virtual commodities of the mall;

and the second consumption attribute determining unit is used for determining the consumption attribute of the current user according to the game duration data and the browsing data.

In a preferred embodiment, the statistical analysis module 23 specifically includes:

a first data value degree obtaining unit, configured to perform value analysis on the consumption data of the current user and the consumption data of the other users based on an RFM model, to obtain a data value degree of each consumption data;

and the first user value type acquisition unit is used for carrying out statistical analysis on the data value degree of each consumption data to obtain the user value type of the current user.

In a preferred embodiment, the statistical analysis module 23 specifically includes:

a second data value degree obtaining unit, configured to perform value analysis on the consumption data of the current user based on an RFM model, to obtain a data value degree of each consumption data of the current user;

the standard data value degree acquisition unit is used for carrying out value analysis on the consumption data of other users based on an RFM model to obtain the standard data value degree of each consumption data;

the data value degree comparison unit is used for comparing the data value degree of each consumption data of the current user with the standard data value degree of each consumption data, and judging the data value degree of the consumption data with the comparison result of less than the standard data value degree as the non-abnormal data value degree;

and the second user value type acquisition unit is used for carrying out statistical analysis on the non-abnormal data value degree of each consumption data to obtain the user value type of the current user.

Optionally, when the data value degrees are compared, the data value degrees within the preset range of the standard data value degrees may be used as non-abnormal data value degrees, and the data value degrees outside the preset range may be used as abnormal data value degrees.

Furthermore, after the abnormal data value degree is obtained, the abnormal data value degree can be corrected through the standard data value degree.

In a preferred embodiment, the commodity recommending module 24 specifically includes:

the to-be-recommended commodity obtaining unit is used for obtaining a corresponding to-be-recommended commodity according to the user value type and based on a preset mapping relation between the user value type and the recommended commodity;

the target commodity selecting unit is used for selecting the first N commodities in all the commodities to be recommended as target commodities; n is an integer and is greater than or equal to 1;

and the target commodity recommending unit is used for recommending the target commodity to the current user.

The value of N may be preset or may be modified by a user.

Fig. 3 is a schematic structural diagram of a preferred embodiment of a commodity recommendation device based on user value according to the present invention, where the commodity recommendation device can implement all the processes of the commodity recommendation method based on user value according to any of the above embodiments and achieve corresponding technical effects.

As shown in fig. 3, the article recommending apparatus includes:

a memory 31 for storing a computer program;

a processor 32 for executing the computer program;

wherein the processor 32, when executing the computer program, implements the method for recommending goods based on user value according to any of the above embodiments.

Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 31 and executed by the processor 32 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the goods recommending apparatus.

The Processor 32 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 31 may be used to store the computer programs and/or modules, and the processor 32 implements various functions of the goods recommending apparatus by running or executing the computer programs and/or modules stored in the memory 31 and calling data stored in the memory 31. The memory 31 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory 31 may include a high speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.

It should be noted that the above-mentioned product recommendation device includes, but is not limited to, a processor and a memory, and those skilled in the art will understand that the structural diagram of fig. 3 is only an example of the above-mentioned product recommendation device, and does not constitute a limitation of the product recommendation device, and may include more components than those shown in the drawing, or combine some components, or different components.

The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and it should be noted that, for those skilled in the art, several equivalent obvious modifications and/or equivalent substitutions can be made without departing from the technical principle of the present invention, and these obvious modifications and/or equivalent substitutions should also be regarded as the scope of the present invention.