CN110992095B - Consumer portrait generation method and device - Google Patents
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Abstract
The invention provides a consumer portrait generation method and a device, which relate to the technical field of data processing, and are used for obtaining first purchase information of a first user in a first shop, wherein the first shop is a shop on a first e-commerce platform; obtaining first user information according to the first purchase information; obtaining second user portrait information of a second user; obtaining second purchase information of a second user according to the second user portrait information; judging whether the second purchase information and the first purchase information have first similarity or not; when the second purchase information and the first purchase information have first similarity, the first user information and the second user portrait information are compared to obtain first user portrait information, and therefore the technical effects that commodity information purchased by a consumer is directly added into an existing consumer portrait database, portrait is optimized, and portrait accuracy is improved are achieved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a consumer portrait generation method and device.
Background
The portrait is based on a big data platform, and a user is comprehensively evaluated by an automatic learning and identifying method of an acquisition machine, wherein for the user with the tag data, a prediction model established by a system is generally adopted for user identification; and for the users without the label data, deep mining is carried out according to a data mining analysis algorithm to identify the relationship of the users, so that the real-time and automatic depiction of the user feature library is realized. Consumer representation refers to a user representation of a user population on an e-commerce platform.
However, the applicant of the present invention finds that the prior art has at least the following technical problems:
the defined label information cannot completely contain all the characteristic information of the consumer, and the accuracy of the prediction model is low, and the accuracy of the representation is low.
Disclosure of Invention
The embodiment of the invention provides a consumer portrait generation method and device, solves the technical problems that label information defined in the prior art cannot completely contain all characteristic information of consumers, the accuracy of a prediction model is low, and the portrait accuracy is low, and achieves the technical effects of directly adding commodity information purchased by the consumers into an existing consumer portrait database, optimizing the portrait, and improving the portrait accuracy.
In view of the foregoing, embodiments of the present application are provided to provide a consumer representation generation method and apparatus.
In a first aspect, the present invention provides a consumer representation generation method, the method comprising: obtaining first purchase information of a first user in a first shop, wherein the first shop is a shop on a first e-commerce platform; obtaining first user information according to the first purchase information; obtaining second user portrait information for a second user; obtaining second purchase information of a second user according to the second user portrait information; judging whether the second purchase information and the first purchase information have first similarity or not; and when the second purchase information and the first purchase information have first similarity, comparing the first user information with the second user portrait information to obtain first user portrait information.
Preferably, the determining whether the second purchase information and the first purchase information have a first similarity includes:
obtaining first consumption level information of a first user; obtaining second consumption level information of a second user; determining whether the first consumption level is the same as the second consumption level; when the first consumption level is the same as the second consumption level, determining a first commodity category in the first purchase information and a second commodity category in the second purchase information; judging whether the first commodity category is the same as the second commodity category; determining that the second purchase information is the same as the first purchase information when the first item category is the same as the second item category.
Preferably, the comparing the first user information with the second user portrait information to obtain first user portrait information includes:
determining first user characteristic information according to the first user information and the first purchase information; and comparing the first user characteristic information with the second user portrait information to obtain first user portrait information.
Preferably, the method further comprises:
determining a first price of a first commodity according to the first purchase information; determining a first commodity category according to the first purchase information; determining first portrait characteristic information according to the first commodity category and the first price; and comparing the first portrait characteristic information with the second user portrait information to obtain first user portrait information.
Preferably, the comparing the first portrait feature information with the second user portrait information to obtain first user portrait information includes:
determining first order address information according to the first purchase information; determining address information of a first user according to the first order address information; determining second portrait characteristic information according to the address information of the first user; determining cross feature information based on the first portrait feature information and the second portrait feature information; and comparing the cross feature information with the second user portrait information to obtain first user portrait information.
Preferably, the method further comprises:
obtaining first marketing short message information received by a first user, wherein the first marketing short message information is marketing short message information of a first shop; determining a first purchase conversion rate according to the first marketing short message information and the first purchase information; and obtaining first user portrait information according to the first purchase conversion rate and the second user portrait information.
In a second aspect, the present invention provides a consumer representation generation apparatus, the apparatus comprising:
a first obtaining unit, configured to obtain first purchase information of a first user in a first store, where the first store is a store on a first e-commerce platform;
a second obtaining unit, configured to obtain first user information according to the first purchase information;
a third obtaining unit configured to obtain second user portrait information of a second user;
a fourth obtaining unit, configured to obtain second purchase information of a second user according to the second user portrait information;
a first judging unit configured to judge whether the second purchase information and the first purchase information have a first similarity;
a fifth obtaining unit, configured to compare the first user information with the second user portrait information to obtain first user portrait information when the second purchase information has a first similarity with the first purchase information.
Preferably, the determining, by the first determining unit, whether the second purchase information and the first purchase information have a first similarity includes:
a sixth obtaining unit configured to obtain first consumption level information of the first user;
a seventh obtaining unit configured to obtain second consumption level information of a second user;
a second determination unit configured to determine whether the first consumption level and the second consumption level are the same;
a first determining unit configured to determine a first item category in the first purchase information and a second item category in the second purchase information when the first consumption level is the same as the second consumption level;
a third determination unit configured to determine whether the first item category is the same as the second item category;
a second determination unit configured to determine that the second purchase information is the same as the first purchase information when the first item category is the same as the second item category.
Preferably, the comparing the first user information with the second user portrait information in the fifth obtaining unit to obtain the first user portrait information includes:
a third determining unit, configured to determine first user feature information according to the first user information and the first purchase information;
an eighth obtaining unit, configured to compare the first user feature information with the second user portrait information to obtain first user portrait information.
Preferably, the apparatus further comprises:
a fourth determining unit configured to determine a first price of the first item according to the first purchase information;
a fifth determining unit configured to determine a first item category according to the first purchase information;
a sixth determining unit, configured to determine first portrait feature information according to the first item category and the first price;
a ninth obtaining unit to compare the first portrait feature information with the second user portrait information to obtain first user portrait information.
Preferably, the ninth obtaining unit compares the first portrait feature information with the second user portrait information to obtain first user portrait information, and includes:
a seventh determining unit, configured to determine first order address information according to the first purchase information;
an eighth determining unit, configured to determine address information of the first user according to the first order address information;
a ninth determining unit for determining second portrait feature information based on address information of the first user;
a tenth determination unit to determine cross feature information from the first portrait feature information and the second portrait feature information;
a tenth obtaining unit, configured to compare the cross feature information with the second user portrait information, and obtain first user portrait information.
Preferably, the apparatus further comprises:
an eleventh obtaining unit, configured to obtain first marketing short message information received by a first user, where the first marketing short message information is marketing short message information of a first store;
an eleventh determining unit, configured to determine a first purchase conversion rate according to the first marketing short message information and the first purchase information;
a twelfth obtaining unit, configured to obtain first user portrait information according to the first purchase conversion rate and the second user portrait information.
In a third aspect, the present invention provides a consumer representation generating apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any of the above methods when the program is executed.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
according to the method and the device for generating the consumer portrait, first purchase information of a first user in a first shop is obtained, wherein the first shop is a shop on a first e-commerce platform; obtaining first user information according to the first purchase information; obtaining second user portrait information of a second user; obtaining second purchase information of a second user according to the second user portrait information; judging whether the second purchase information and the first purchase information have first similarity or not; when the second purchase information and the first purchase information have first similarity, the first user information and the second user portrait information are compared to obtain first user portrait information, so that the technical problems that label information defined in the prior art cannot completely contain all feature information of consumers, the accuracy of a prediction model is low, and the portrait accuracy is low are solved, and the technical effects that commodity information purchased by the consumers is directly added into an existing consumer portrait database, portrait is optimized, and portrait accuracy is improved are achieved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a method for generating a consumer representation in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a consumer representation generation apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another embodiment of a consumer representation generation apparatus.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a first judging unit 15, a fifth obtaining unit 16, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the invention provides a consumer portrait generation method and device, which are used for solving the technical problems that label information defined in the prior art cannot completely contain all characteristic information of consumers, the precision of a prediction model is low, and the portrait accuracy is low.
The technical scheme provided by the invention has the following general idea: obtaining first purchase information of a first user in a first shop, wherein the first shop is a shop on a first e-commerce platform; obtaining first user information according to the first purchase information; obtaining second user portrait information of a second user; obtaining second purchase information of a second user according to the second user portrait information; judging whether the second purchase information and the first purchase information have first similarity or not; when the second purchase information and the first purchase information have first similarity, the first user information and the second user portrait information are compared to obtain first user portrait information, and therefore the technical effects that commodity information purchased by a consumer is directly added into an existing consumer portrait database, portrait optimization is achieved, and portrait accuracy is improved are achieved.
The technical solutions of the present invention are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present invention are described in detail in the technical solutions of the present application, and are not limited to the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
Example one
FIG. 1 is a flowchart illustrating a method for generating a consumer representation according to an embodiment of the present invention. As shown in FIG. 1, an embodiment of the present invention provides a consumer representation generation method, including:
step 110: first purchase information of a first user at a first store is obtained, wherein the first store is a store on a first e-commerce platform.
Step 120: and obtaining first user information according to the first purchase information.
Specifically, in the consumer portrait generation method in the embodiment of the application, when a first user purchases a commodity in a shop, first user information is obtained through first purchase information, second user portrait information of other users in a database is obtained, second purchase information of the second user is obtained, when the first purchase information of the first user is the same as the second purchase information of the second user, the first user information is compared with the second user portrait information to obtain first portrait information of the first user, and portrait of the first user can be optimized by combining self attribute characteristics and purchase characteristics of the first user, so that portrait accuracy is improved. First, when a first user purchases a commodity in a first shop on a first e-commerce platform, first purchase information of the first user is obtained, wherein the first purchase information includes image-text information of the first commodity purchased by the first user and first user purchase characteristic information, and the image-text information of the first commodity is a picture of the first commodity in the first purchase information and a text description of the first commodity, such as a price, an action, an advantage, a material, a use method and the like of the first commodity. The first user purchase characteristic information is order information of the first user for purchasing the first commodity, and the like. And obtaining first user information according to the first purchase information, wherein the first user information comprises inherent attribute characteristic information and purchase behavior characteristic information of the first user, such as identity, gender, birthday, region and the like in the inherent attribute characteristic information of the first user, and the first user is a 20-year-old female college student from the adult. The first user purchases the clothing of the S code, and the frequently purchased goods are contact lenses.
Step 130: second user portrait information for a second user is obtained.
Step 140: and obtaining second purchase information of a second user according to the second user portrait information.
Specifically, the second user figure information is image information automatically generated by the purchase characteristics of the second user. And obtaining second user portrait information of other users on the E-commerce platform stored in the graph database, and obtaining second purchase information of the second user according to the second user portrait information, wherein the second purchase information comprises the graphic information of a second commodity purchased by the second user and the purchase characteristic information of the second user. The image-text information of the second commodity, that is, the second purchase information, includes a picture of the second commodity and a text description of the second commodity, such as a price, an action, an advantage, a material, a use method, and the like of the second commodity. The second user purchase feature information is order information of the second user for purchasing the second commodity and the like.
Step 150: and judging whether the second purchase information and the first purchase information have first similarity or not.
Further, the determining whether the second purchase information and the first purchase information have a first similarity includes: obtaining first consumption level information of a first user; obtaining second consumption level information of a second user; determining whether the first consumption level is the same as the second consumption level; when the first consumption level is the same as the second consumption level, determining a first commodity category in the first purchase information and a second commodity category in the second purchase information; judging whether the first commodity category is the same as the second commodity category; determining that the second purchase information is the same as the first purchase information when the first item category is the same as the second item category.
Specifically, after the second purchase information of the second user is obtained, it is further determined whether the second purchase information of the second user and the first purchase information of the first user have the first similarity. According to the embodiment of the application, whether the consumption level of the user is the same as the category of the commodity is mainly judged, and therefore it is determined that the second purchasing information of the second user has first similarity with the first purchasing information of the first user. By obtaining the first consumption level information of the first user and the second consumption level information of the second user, the shopping amount of the first user on the first e-commerce platform and the shopping amount of the second user on the second e-commerce platform are obtained. And judging whether the first consumption level of the first user and the second consumption level of the second user are the same or in the same consumption level interval, and respectively determining the first commodity category in the first purchase information and the second commodity category in the second purchase information when the consumption levels of the first user and the second user are the same or in the same consumption level interval. For example, the first consumption level of the first user is 2200 yuan/month, the second consumption level of the second user is 2350 yuan/month, the difference value between the first consumption level and the second consumption level is 150 yuan/quarter, and the consumption level interval is 300 yuan/quarter, so that the consumption levels of the first user and the second user are in the same consumption level interval, and the category of the first commodity purchased by the first user and the category of the second commodity purchased by the second user are further determined. And comparing whether the first commodity category is the same as the second commodity category, wherein when the first commodity category is the same as the second commodity category, the second purchase information is the same as the first purchase information. That is to say, the two purchased commodities have the same category, such as clothes, pregnant and baby articles, kitchen ware, sanitary articles, and the like, the two purchased commodities have the same purchase information and the same purchase behavior characteristics, that is, the second purchase information has the first similarity with the first purchase information.
Step 160: and when the second purchase information and the first purchase information have first similarity, comparing the first user information with the second user portrait information to obtain first user portrait information.
Further, the comparing the first user information with the second user portrait information to obtain first user portrait information includes: determining first user characteristic information according to the first user information and the first purchase information; and comparing the first user characteristic information with the second user portrait information to obtain first user portrait information.
Specifically, when the second purchase information has a first similarity with the first purchase information, the inherent attribute feature information of the first user may be determined according to the first user information, and the purchase behavior feature of the first user may be determined according to the first purchase information, where the first user feature information includes the inherent attribute feature information and the purchase behavior feature, and the first user feature information is a combination of the personal feature and the purchase behavior feature of the first user, which are comprehensive and comprehensive. And placing the first user characteristic information of the first user into a database to be compared with the second user portrait information to obtain first user portrait information, and repeatedly comparing the first user portrait information according to the first user characteristic information to optimize the portrait information.
Therefore, according to the consumer portrait generation method in the embodiment, when a first user purchases a commodity in a shop, first user information is obtained through the first purchase information, second user portrait information of other users in the database is obtained, second purchase information of the second user is obtained, when the first purchase information of the first user is the same as the second purchase information of the second user, the first user information is compared with the second user portrait information to obtain first portrait information of the first user, and portrait of the first user can be optimized by combining self attribute characteristics and purchase characteristics of the first user, so that portrait accuracy is improved, and technical problems that label information defined in the prior art cannot completely contain all characteristic information of the consumers, accuracy of a prediction model is low, and portrait accuracy is low are solved.
Furthermore, the data fusion method in this embodiment may also be implemented by combining an Artificial Intelligence technology, wherein Artificial Intelligence (AI) is also called machine Intelligence, which is a subject for researching a computer to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, and the like) of a human, and mainly includes a principle that the computer realizes Intelligence, and a computer similar to human brain Intelligence is manufactured, so that the computer can realize higher-level application. The method comprises the following specific steps: obtaining a photo of first purchase information, wherein the photo of first purchase information includes first user information; inputting the picture of the first purchase information into a model, wherein the model is obtained by machine learning training by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the first purchase information, the first identification information used for identifying the purchase information of the first user and the second user is the same, and the second label information used for identifying the first user and the second user portrait information are the same; acquiring output information of the model, wherein the output information is first user portrait information; the output information of the model is the same as the first purchase information of the first user by utilizing the second purchase information of the second user, so that the first user information and the second user portrait information are compared, namely the first user characteristic information and the second user portrait information are compared to obtain the first user portrait information.
Further, the training model in this embodiment is obtained by using machine learning training with multiple sets of data, where machine learning is a way to implement artificial intelligence, and has a certain similarity with data mining, and is also a multi-domain cross subject, and relates to multiple subjects such as probability theory, statistics, approximation theory, convex analysis, and computation complexity theory. Compared with the method for finding mutual characteristics among big data by data mining, the machine learning focuses more on the design of an algorithm, so that a computer can learn rules from the data in a blank manner, and unknown data can be predicted by using the rules.
Further, the method further comprises: determining a first price of a first commodity according to the first purchase information; determining a first commodity category according to the first purchase information; determining first portrait characteristic information according to the first commodity category and the first price; and comparing the first portrait characteristic information with the second portrait information to obtain first portrait information of the user.
Further, the comparing the first portrait feature information with the second user portrait information to obtain first user portrait information includes: determining first order address information according to the first purchase information; determining address information of a first user according to the first order address information; determining second portrait feature information according to the address information of the first user; determining cross feature information based on the first portrait feature information and the second portrait feature information; and comparing the cross feature information with the second user portrait information to obtain first user portrait information.
Specifically, a first price of a first commodity purchased by a first user is obtained according to first purchase information of the first user, a commodity category to which the first commodity belongs is determined, a first image feature of the first user can be directly determined according to the first price of the first commodity and the first commodity category, namely, an identity feature and a purchase behavior feature of the first user can be determined according to the purchased commodity price and the commodity category, the first image feature of the first user is further determined, and first user image information is obtained through the first image feature. For example, the price of the same category of commodities purchased by the first user belongs to middle and high grades, and household articles, living articles and parts of children articles are purchased more, the user is inferred to be a female aged about 35 years, and the portrait information of the first user can be directly depicted through the graph database. When the first user portrait information is obtained, first order address information of the first user is obtained at the same time, and the address information of the first user can be determined from the first order address information, for example, the address of the first user is suzhou. The method comprises the steps of determining second portrait characteristic information according to address information of a first user, fusing the first portrait characteristic information and the second portrait characteristic information according to different regional differences and regional characteristics of people of all the regions to obtain cross characteristic information of the first portrait characteristic information and the second portrait characteristic information, comparing the cross characteristic information with the second user portrait information, and optimizing the first user portrait information to enable portrait to be more accurate.
Further, the method further comprises: obtaining first marketing short message information received by a first user, wherein the first marketing short message information is marketing short message information of a first shop; determining a first purchase conversion rate according to the first marketing short message information and the first purchase information; and obtaining first user portrait information according to the first purchase conversion rate and the second user portrait information.
Specifically, by obtaining first marketing short message information sent by a first shop and received by a first user, the first marketing short message information includes marketing activities of a first commodity, such as discount information, coupons, full and reduced activities, and the like. After the first user receives the first marketing short message for a period of time, first purchase information of the first commodity purchased by the first user is obtained. And determining a first purchase conversion rate through the first marketing short message information and the first purchase information, wherein if the first shop sends the first marketing short message to 5 general users, and 2 general users purchase the first commodity, the first purchase conversion rate is 40%. And if the purchasing behavior characteristics of the first user can be determined according to the first purchasing conversion rate, and the purchasing behavior characteristics belong to impulse type purchasing or just-needed type purchasing, and the like, comparing the purchasing behavior characteristics of the first user with the second user portrait information to obtain the first user portrait information.
Example two
Based on the same inventive concept as the consumer representation generation method in the foregoing embodiment, the present invention further provides a consumer representation generation method apparatus, as shown in fig. 2, the apparatus includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first purchase information of a first user in a first store, where the first store is a store on a first e-commerce platform;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first user information according to the first purchase information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain second user portrait information of a second user;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain second purchase information of a second user according to the second user portrait information;
a first judging unit 15, where the first judging unit 15 is configured to judge whether the second purchase information and the first purchase information have a first similarity;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to compare the first user information with the second user portrait information to obtain first user portrait information when the second purchase information has a first similarity with the first purchase information.
Further, the determining, by the first determining unit, whether the second purchase information and the first purchase information have a first similarity includes:
a sixth obtaining unit configured to obtain first consumption level information of the first user;
a seventh obtaining unit configured to obtain second consumption level information of a second user;
a second determination unit configured to determine whether the first consumption level and the second consumption level are the same;
a first determining unit configured to determine a first item category in the first purchase information and a second item category in the second purchase information when the first consumption level is the same as the second consumption level;
a third determination unit configured to determine whether the first commodity category is the same as the second commodity category;
a second determination unit configured to determine that the second purchase information is the same as the first purchase information when the first item category is the same as the second item category.
Further, the comparing, by the fifth obtaining unit, the first user information with the second user portrait information to obtain first user portrait information includes:
a third determining unit, configured to determine first user feature information according to the first user information and the first purchase information;
an eighth obtaining unit, configured to compare the first user characteristic information with the second user portrait information, and obtain first user portrait information.
Further, the apparatus further comprises:
a fourth determining unit configured to determine a first price of the first item according to the first purchase information;
a fifth determining unit configured to determine a first commodity category according to the first purchase information;
a sixth determining unit, configured to determine first portrait feature information according to the first item category and the first price;
a ninth obtaining unit, configured to compare the first portrait feature information with the second user portrait information to obtain first user portrait information.
Further, the ninth obtaining unit compares the first portrait feature information with the second user portrait information to obtain first user portrait information, and includes:
a seventh determining unit, configured to determine first order address information according to the first purchase information;
an eighth determining unit, configured to determine address information of the first user according to the first order address information;
a ninth determining unit for determining second portrait feature information based on address information of the first user;
a tenth determination unit to determine cross feature information from the first portrait feature information and the second portrait feature information;
a tenth obtaining unit, configured to compare the cross feature information with the second user portrait information to obtain first user portrait information.
Further, the apparatus further comprises:
an eleventh obtaining unit, configured to obtain first marketing short message information received by a first user, where the first marketing short message information is marketing short message information of a first store;
an eleventh determining unit, configured to determine a first purchase conversion rate according to the first marketing short message information and the first purchase information;
a twelfth obtaining unit, configured to obtain first user portrait information according to the first purchase conversion rate and the second user portrait information.
Various variations and specific examples of a consumer representation generation method in the first embodiment of fig. 1 are also applicable to a consumer representation generation apparatus of the present embodiment, and a person skilled in the art can clearly know an implementation method of a consumer representation generation apparatus in the present embodiment through the foregoing detailed description of a consumer representation generation method, so for the brevity of the description, detailed descriptions are omitted here.
EXAMPLE III
Based on the same inventive concept as one of the consumer representation generation methods in the previous embodiments, the present invention further provides a consumer representation generation apparatus, as shown in fig. 3, comprising a memory 304, a processor 302, and a computer program stored in the memory 304 and executable on the processor 302, wherein the processor 302, when executing the program, implements the steps of any one of the methods of the consumer representation generation methods described above.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be one and the same element, i.e. a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
Example four
Based on the same inventive concept as the consumer representation generation method in the previous embodiment, the present invention further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of: obtaining first purchase information of a first user in a first shop, wherein the first shop is a shop on a first e-commerce platform; obtaining first user information according to the first purchase information; obtaining second user portrait information for a second user; obtaining second purchase information of a second user according to the second user portrait information; judging whether the second purchase information and the first purchase information have first similarity or not; and when the second purchase information and the first purchase information have first similarity, comparing the first user information with the second user portrait information to obtain first user portrait information.
In a specific implementation, when the program is executed by a processor, any method step in the first embodiment may be further implemented.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
according to the method and the device for generating the consumer portrait, first purchase information of a first user in a first shop is obtained, wherein the first shop is a shop on a first e-commerce platform; obtaining first user information according to the first purchase information; obtaining second user portrait information for a second user; obtaining second purchase information of a second user according to the second user portrait information; judging whether the second purchase information and the first purchase information have first similarity or not; when the second purchase information and the first purchase information have first similarity, the first user information and the second user portrait information are compared to obtain first user portrait information, so that the technical problems that label information defined in the prior art cannot completely contain all feature information of consumers, the accuracy of a prediction model is low, and the portrait accuracy is low are solved, and the technical effects that commodity information purchased by the consumers is directly added into an existing consumer portrait database, portrait is optimized, and portrait accuracy is improved are achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A consumer representation generation method, the method comprising:
obtaining first purchase information of a first user in a first shop, wherein the first shop is a shop on a first e-commerce platform;
obtaining first user information according to the first purchase information;
obtaining second user portrait information for a second user;
obtaining second purchase information of a second user according to the second user portrait information;
judging whether the second purchase information and the first purchase information have first similarity or not;
and when the second purchase information and the first purchase information have first similarity, comparing the first user information with the second user portrait information to obtain first user portrait information.
2. The method of claim 1, wherein said determining whether the second purchase information has a first similarity to the first purchase information comprises:
obtaining first consumption level information of a first user;
obtaining second consumption level information of a second user;
determining whether the first consumption level is the same as the second consumption level;
when the first consumption level is the same as the second consumption level, determining a first commodity category in the first purchase information and a second commodity category in the second purchase information;
judging whether the first commodity category is the same as the second commodity category;
determining that the second purchase information is the same as the first purchase information when the first item category is the same as the second item category.
3. The method of claim 1, wherein comparing the first user information with the second user representation information to obtain first user representation information comprises:
determining first user characteristic information according to the first user information and the first purchase information;
and comparing the first user characteristic information with the second user portrait information to obtain first user portrait information.
4. The method of claim 1, wherein the method further comprises:
determining a first price of a first commodity according to the first purchase information;
determining a first commodity category according to the first purchase information;
determining first portrait feature information according to the first commodity category and the first price;
and comparing the first portrait characteristic information with the second portrait information to obtain first portrait information of the user.
5. The method of claim 4, wherein said comparing said first representation feature information to said second user representation information to obtain first user representation information comprises:
determining first order address information according to the first purchase information;
determining address information of a first user according to the first order address information;
determining second portrait feature information according to the address information of the first user;
determining cross feature information based on the first portrait feature information and the second portrait feature information;
and comparing the cross feature information with the second user portrait information to obtain first user portrait information.
6. The method of claim 1, wherein the method further comprises:
obtaining first marketing short message information received by a first user, wherein the first marketing short message information is marketing short message information of a first shop;
determining a first purchase conversion rate according to the first marketing short message information and the first purchase information;
and obtaining first user portrait information according to the first purchase conversion rate and the second user portrait information.
7. A consumer representation generation apparatus, the apparatus comprising:
a first obtaining unit, configured to obtain first purchase information of a first user in a first store, where the first store is a store on a first e-commerce platform;
a second obtaining unit, configured to obtain first user information according to the first purchase information;
a third obtaining unit configured to obtain second user portrait information of a second user;
a fourth obtaining unit, configured to obtain second purchase information of a second user according to the second user portrait information;
a first judging unit configured to judge whether the second purchase information and the first purchase information have a first similarity;
a fifth obtaining unit, configured to, when the second purchase information and the first purchase information have a first similarity, compare the first user information with second user portrait information to obtain first user portrait information.
8. A consumer representation generation apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1 to 6 when executing the program.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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