CN111882409A - Method and device for recommending main body and electronic equipment - Google Patents
Method and device for recommending main body and electronic equipment Download PDFInfo
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- CN111882409A CN111882409A CN202011040331.XA CN202011040331A CN111882409A CN 111882409 A CN111882409 A CN 111882409A CN 202011040331 A CN202011040331 A CN 202011040331A CN 111882409 A CN111882409 A CN 111882409A
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Abstract
The invention provides a method and a device for recommending a subject and electronic equipment, wherein the method comprises the following steps: reducing the commodity information of the commodities to be sold in the target main body, and extracting effective attribute features of the commodities to be sold; acquiring shopping information of a target user, and extracting shopping behavior characteristics corresponding to a target commodity; and determining the probability of the target user for purchasing the commodities to be sold according to the effective attribute characteristics and the shopping behavior characteristics of the commodities to be sold, and recommending a target subject to the target user when the probability meets a preset condition. According to the technical scheme provided by the embodiment of the invention, the target subject is recommended based on the probability of the target user purchasing the commodity to be sold, and a subject which is more interested in the target subject can be recommended to the user; the effective attribute characteristics determined after reduction can accurately represent the characteristics of the commodity to be sold through a small amount of attribute characteristics, so that whether the target user is possible to purchase the commodity to be sold or not can be conveniently, quickly and accurately determined subsequently, the processing amount can be reduced, and the processing efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of personalized recommendation, in particular to a method and a device for recommending a subject, electronic equipment and a computer-readable storage medium.
Background
In recent years, with the rapid development of e-commerce platforms and electronic shopping, each large e-commerce platform accumulates a great amount of real transaction data while completing hundreds of millions of transaction amounts. In order to realize accurate marketing, how to predict the future purchase demand of a user based on big data application is a key problem and is a core technology required by all e-commerce platforms or shopping websites.
Direct seeding is a new generation of delivery method. The network shopping anchor is relatively weak in supervision and low in threshold, and most practitioners are mainly young. The exaggerated personalized crowd is particularly concerned by various hunter young people, and particularly talented anchor among the people has a plurality of fans which only trust the anchor and ignore the product quality, thereby causing the phenomenon of 'bad coin dispelling good coin'. However, the existing personalized anchor recommendation technology generally recommends an anchor to a user according to the similarity between the anchors, so that the quality of a product sold by the recommended anchor is prone to be problematic, and the current recommendation method cannot recommend a shopping anchor to the user in a personalized manner.
Disclosure of Invention
In order to solve the existing technical problem, embodiments of the present invention provide a method and an apparatus for recommending a subject, an electronic device, and a computer-readable storage medium.
In a first aspect, an embodiment of the present invention provides a method for recommending a subject, including:
acquiring commodity information of commodities to be sold in a target main body, carrying out reduction processing on the commodity information, and extracting effective attribute features of the commodities to be sold;
the method comprises the steps of obtaining shopping information of a target user, and extracting shopping behavior characteristics corresponding to a target commodity, wherein the shopping behavior characteristics comprise a shopping behavior identifier and attribute characteristics of the target commodity;
and determining the probability of the target user for purchasing the commodity to be sold according to the effective attribute characteristics of the commodity to be sold and the shopping behavior characteristics, and recommending the target subject to the target user when the probability meets a preset condition.
In a second aspect, an embodiment of the present invention further provides an apparatus for recommending a subject, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring commodity information of commodities to be sold in a target main body, reducing the commodity information and extracting effective attribute characteristics of the commodities to be sold;
the second acquisition module is used for acquiring the shopping information of a target user and extracting the shopping behavior characteristics corresponding to the target commodity, wherein the shopping behavior characteristics comprise a shopping behavior identifier and the attribute characteristics of the target commodity;
and the processing module is used for determining the probability of the target user for purchasing the commodity to be sold according to the effective attribute characteristics of the commodity to be sold and the shopping behavior characteristics, and recommending the target main body to the target user when the probability meets preset conditions.
In a third aspect, an embodiment of the present invention provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the steps in the method for recommending a subject are implemented.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for recommending a subject according to any one of the above items.
According to the method, the device, the electronic equipment and the computer-readable storage medium for recommending the main body, the effective attribute characteristics of the commodity to be sold are extracted by carrying out reduction processing on the commodity information; after determining the shopping behavior characteristics of the target user, whether the target user is likely to purchase the goods for sale is determined based on the effective attribute characteristics and the shopping behavior characteristics, and then whether the target subject is recommended to the target user can be determined. The method and the device have the advantages that the target subject is recommended to the target user based on the probability that the target user purchases the commodity for sale, and the subject which is more interested in the target subject can be recommended to the user; the effective attribute characteristics determined after reduction can accurately represent the characteristics of the commodity to be sold through a small amount of attribute characteristics, so that whether the target user is possible to purchase the commodity to be sold or not can be conveniently, quickly and accurately determined subsequently, the processing amount can be reduced, and the processing efficiency can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present invention, the drawings required to be used in the embodiments or the background art of the present invention will be described below.
FIG. 1 is a flow chart illustrating a method for recommending a subject according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an apparatus for recommending a subject according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for executing a method for recommending a subject according to an embodiment of the present invention.
Detailed Description
In the description of the embodiments of the present invention, it should be apparent to those skilled in the art that the embodiments of the present invention can be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, embodiments of the invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied in the medium.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only Memory (ROM), an erasable programmable read-only Memory (EPROM), a Flash Memory, an optical fiber, a compact disc read-only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any combination thereof. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or apparatus.
The computer program code embodied on the computer readable storage medium may be transmitted using any appropriate medium, including: wireless, wire, fiber optic cable, Radio Frequency (RF), or any suitable combination thereof.
Computer program code for carrying out operations for embodiments of the present invention may be written in assembly instructions, Instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages, including an object oriented programming language, such as: java, Smalltalk, C + +, and also include conventional procedural programming languages, such as: c or a similar programming language. The computer program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be over any of a variety of networks, including: a Local Area Network (LAN) or a Wide Area Network (WAN), which may be connected to the user's computer, may be connected to an external computer.
The method, the device and the electronic equipment are described through the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiments of the present invention will be described below with reference to the drawings.
Fig. 1 is a flowchart illustrating a method for recommending a subject according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step 101: and acquiring commodity information of the commodities to be sold in the target main body, reducing the commodity information, and extracting effective attribute features of the commodities to be sold.
In the embodiment of the invention, the target main body is a main body for selling commodities, such as a live broadcast room and the like, and the commodities to be sold by the target main body are to-be-sold commodities; one target main body can correspond to one commodity to be sold and can also correspond to a plurality of commodities to be sold; that is, one target body may sell one kind of product, and may also sell a plurality of kinds of products, which is not limited in this embodiment. If the target subject is a live broadcast room, since the live broadcast room generally performs advance notice, the commodities to be sold can be determined according to the advance notice of the live broadcast room, and then the commodity information of each commodity to be sold is determined in a network crawling mode and the like.
In this embodiment, the commodity information of the commodity to be sold is information related to the attribute of the commodity to be sold, and may specifically include the name, brand, model, belonging category, commodity price, belonging price interval, sales volume, function, and the like of the commodity to be sold; correspondingly, the attribute characteristics of the commodity to be sold can be determined based on the commodity information, and the attribute characteristics specifically comprise one or more items of brands, models, categories, commodity prices, price intervals, sales volumes and functions; typically, there are multiple attribute features for each item to be sold. Since there may be a plurality of attribute features of the product, but not all the attribute features may affect the purchasing behavior of the user, in this embodiment, through the reduction process, the attribute features that affect the purchasing behavior of the user, that is, the effective attribute features, are extracted, and then, it may be determined whether the target user may purchase the product to be sold based on the effective attribute features of the product to be sold.
In addition, optionally, because the number of the main bodies such as the live broadcast room is large, the main bodies are pre-screened in the embodiment to select the target main body. Specifically, before the step 101 "obtaining the commodity information of the commodity to be sold in the target main body", the method further comprises: and auditing the subject to be determined according to the sales information of the subject to be determined, and taking the subject to be determined as a target subject when the audit is passed.
In this embodiment, a plurality of pending main bodies may be predetermined, and the sales information of each pending main body is determined, where the sales information includes information such as the number, evaluation, and after-sale service evaluation of the goods sold by the pending main body; meanwhile, an audit standard is preset, and then audit is carried out based on the sales information, if the audit is passed, the undetermined subject is indicated to be in accordance with the audit standard, and at the moment, the undetermined subject can be taken as a target subject; otherwise, the undetermined main body is removed. The auditing standard can specifically be that the selling quantity is greater than a certain preset value, the evaluation is higher than a certain preset value, the after-sale service evaluation is higher than a certain preset value, and the like. In this embodiment, the undetermined subject that passes the audit is used as the target subject, and subjects such as a live broadcast room with poor quality can be removed, so that it can be effectively ensured that the target subject is a high-quality subject.
Step 102: the method comprises the steps of obtaining shopping information of a target user, and extracting shopping behavior characteristics corresponding to target commodities, wherein the shopping behavior characteristics comprise shopping behavior identification and attribute characteristics of the target commodities.
In the embodiment of the invention, the target user can generate information related to shopping behaviors, namely shopping information in the shopping process; the shopping behavior specifically includes a browsing behavior, a collecting behavior, a sharing behavior, a trading behavior or a behavior added to a shopping cart, a searching behavior, and the like. Specifically, the target commodity refers to a commodity involved in the shopping behavior of the target user; for example, if the target user purchased article a (there is a transaction behavior) and browsed article B (there is a browsing behavior), both article a and article B are target articles of the target user, and the two correspond to the transaction behavior and the browsing behavior, respectively. In this embodiment, the corresponding shopping behavior feature may be extracted based on the shopping information of the target user, and the shopping behavior feature may represent the shopping behavior of the target user and the attribute of the corresponding target product. For example, when the target user browses the commodity B once, the shopping behavior feature corresponding to the commodity B includes a corresponding shopping behavior identifier, that is, a browsing behavior identifier, and the shopping behavior feature further includes attribute features of the commodity B, such as attribute features of a brand, a model, an affiliated category, a commodity price, an affiliated price interval, a sales volume, a function, and the like. Wherein, the shopping information can directly contain part or all of the attribute characteristics of the target commodity, such as brand, commodity price, etc.; in this embodiment, other attribute features of the target product, such as sales volume and functions, may also be obtained by other manners, such as crawling.
Optionally, the number of users in the actual scene is large, and if all users are taken as target users, the processing amount is increased. Specifically, before the step 102 "obtaining the shopping information of the target user", the method further includes: and determining keywords of the commodities to be sold according to the commodity information of the commodities to be sold in the target main body, and taking the user with the shopping behavior in the preset time period and the keywords correlated as the target user.
In the embodiment of the invention, the keywords contained in the commodity information of the commodity to be sold can be extracted by extracting the keywords, such as commodity names and the like; meanwhile, a preset time period is set, such as the latest day, week and the like, if the user generates the shopping behavior associated with the keyword in the preset time period, it indicates that the user may have a purchase intention, and at this time, the user may be taken as a target user. The shopping behavior associated with the keyword refers to that the shopping behavior points to the commodity associated with the keyword; for example, the user has searched for the keyword, or the user browses a product having the keyword, and the behaviors are shopping behaviors associated with the keyword.
Step 103: and determining the probability of the target user for purchasing the commodities to be sold according to the effective attribute characteristics and the shopping behavior characteristics of the commodities to be sold, and recommending a target subject to the target user when the probability meets a preset condition.
In the embodiment of the invention, the effective attribute characteristics of the to-be-sold commodities can effectively represent whether the user has a purchase intention, and the shopping behavior characteristics of the target user can represent which commodities (namely target commodities or commodities similar to the target commodities) the target user may purchase; after the effective attribute feature and the shopping behavior feature are determined, according to the similarity between the effective attribute feature and the attribute feature of the target commodity, whether the target user is likely to purchase the commodity to be sold or not can be determined, that is, the probability that the target user purchases the commodity to be sold can be determined, if the probability meets a preset condition, the target user is a potential customer, and at the moment, a target subject can be recommended to the target user. The preset condition may specifically be that the probability is higher than a preset value; meanwhile, when the probability corresponding to one or more commodities to be sold meets the preset condition.
According to the method for recommending the main body, provided by the embodiment of the invention, effective attribute characteristics of the commodity to be sold are extracted by carrying out reduction processing on commodity information; after determining the shopping behavior characteristics of the target user, whether the target user is likely to purchase the goods for sale is determined based on the effective attribute characteristics and the shopping behavior characteristics, and then whether the target subject is recommended to the target user can be determined. The method and the device have the advantages that the target subject is recommended to the target user based on the probability that the target user purchases the commodity for sale, and the subject which is more interested in the target subject can be recommended to the user; the effective attribute characteristics determined after reduction can accurately represent the characteristics of the commodity to be sold through a small amount of attribute characteristics, so that whether the target user is possible to purchase the commodity to be sold or not can be conveniently, quickly and accurately determined subsequently, the processing amount can be reduced, and the processing efficiency can be improved.
In addition to the above embodiment, the step 101 "performing reduction processing on the commodity information to extract effective attribute features of the commodity to be sold" includes:
step A1: determining all undetermined attribute characteristics of the commodities to be sold, reducing the characteristics to be determined, and selecting a part from the characteristics to be determined as effective attribute characteristics of the commodities to be sold.
In the embodiment of the invention, after the commodity information of the commodity to be sold is acquired, all attribute characteristics of the commodity to be sold can be extracted, and the attribute characteristics are taken as undetermined attribute characteristics; and then, reducing all the undetermined attribute characteristics, namely selecting part of the undetermined attribute characteristics as effective attribute characteristics of the commodity to be sold. The undetermined attribute features can be reduced based on a rough set theory and the like so as to extract effective attribute features from the undetermined attribute features.
Alternatively, the effective attribute characteristics of the items for sale may be determined based on the shopping behavior characteristics of the target user. In this case, the step 101 of "performing reduction processing on the commodity information to extract effective attribute features of the commodity to be sold" specifically includes:
step B1: determining an attribute feature set of a target commodity corresponding to the shopping behavior features of the target user, and taking a corresponding shopping behavior identifier in the shopping behavior features as a decision feature; the attribute feature set includes a plurality of original attribute features.
In the embodiment of the present invention, as described above, the shopping behavior characteristics may represent the shopping behavior of the target user and the attributes of the corresponding target product; in this embodiment, all attribute features (i.e., original attribute features) of the target product are predetermined, so that an attribute feature set of the target product can be formed. Meanwhile, the shopping behavior identifier can represent the decision degree of the target user on the target commodity, so that the shopping behavior identifier can be used as a decision characteristic; the shopping behavior identifier may be a browsing behavior identifier, a collection behavior identifier, a sharing behavior identifier, a transaction behavior identifier, or a behavior identifier added to a shopping cart, a search behavior identifier, or the like; correspondingly, the decision degree corresponding to the search behavior identification and the browse behavior identification is low, the decision degree corresponding to the collection behavior identification and the share behavior identification is general, the decision degree corresponding to the behavior identification added to the shopping cart is high, and the decision degree corresponding to the transaction behavior identification is highest.
Step B2: and taking an original attribute feature as a target attribute feature, and if the conditional information entropy of the decision feature relative to the attribute feature set is increased when the attribute feature set lacks the target attribute feature, taking the target attribute feature as an effective attribute feature of the target commodity.
In the embodiment of the invention, the attribute feature set of the target commodity comprises a plurality of original attribute features, at the moment, one of the original attribute features is taken as the target attribute feature, if the attribute feature set lacks the target attribute feature, the conditional information entropy of the decision feature relative to the attribute feature set is increased, and at the moment, the condition information entropy is increased due to the lack of the target attribute feature, namely, the conditional information entropy can be reduced by increasing the target attribute feature, so that the decision can be better made; that is, the target attribute feature is useful for decision making, so the target attribute feature can be regarded as a valid attribute feature. On the contrary, if the conditional information entropy of the decision feature relative to the attribute feature set is not changed when the attribute feature set lacks the target attribute feature, it indicates that the target attribute feature does not affect the decision result, and the target attribute feature is useless and can be removed.
Specifically, firstly, the conditional information entropy H0 of the decision attribute relative to the complete attribute feature set is determined, then a target attribute feature is removed, and the conditional information entropy H1 of the decision attribute relative to the attribute feature set after the target attribute feature is removed is determined, and if H1 > H0, the target attribute feature can be used as a valid attribute feature. And if H1= H0, rejecting the target attribute feature.
Step B3: and traversing all original attribute features in the attribute feature set, determining all effective attribute features of the target commodity, and taking the effective attribute features of the target commodity as the effective attribute features of the commodity to be sold.
In the embodiment of the present invention, all the original attribute features in the attribute feature set are traversed, and all the original attribute features are sequentially used as a target attribute feature to execute step B2 described above, so as to determine whether the target attribute feature is an effective attribute feature, and further determine all the effective attribute features of the target product, where the effective attribute feature of the target product can accurately evaluate whether the target user has an intention to purchase the target product. In this case, the attribute features of the product to be sold may also indicate whether the target user wishes to purchase the product to be sold, that is, the valid attribute feature of the target product may be used as the valid attribute feature of the product to be sold. For example, if the original attribute features of the target product include "brand", "product price" and "sales volume", and then "brand" and "product price" are determined to be valid attribute features of the target product, then "brand" and "product price" may also be valid attribute features of the product to be sold.
In the embodiment of the invention, the effective attribute characteristics of the commodity to be sold are determined through the effective attribute characteristics related to the target user, the effective attribute characteristics can better represent the possibility that the target user purchases the commodity to be sold, and the target subject can be recommended to the target user more accurately. In addition, because the number of users is generally much greater than that of the subjects, the effective attribute features of the commodities to be sold are determined based on the effective attribute features related to the target users in the embodiment, the target subjects can be allocated to the target users instead of the target subjects, the processing amount can be greatly reduced, and the subject recommendation can be realized as soon as possible.
Optionally, the step 102 "acquiring the shopping information of the target user and extracting the shopping behavior characteristics corresponding to the target product" includes:
step C1: the method comprises the steps of obtaining shopping information of a target user in a preset time period, carrying out reduction processing on the shopping information, and extracting corresponding shopping behavior characteristics, wherein the shopping behavior characteristics comprise shopping behavior identification and effective attribute characteristics of a reduced target commodity.
In the embodiment of the invention, the shopping information of the target user can be reduced so as to extract the effective attribute characteristics of the target commodity. The process of the reduction processing may specifically refer to the process of determining all effective attribute features of the target commodity in the above steps B1-B3, and the reduction may also be implemented based on a rough set theory, which is not limited in this embodiment.
On the basis of the above embodiment, the step 103 "determining the probability that the target user purchases the product to be sold according to the effective attribute feature and the shopping behavior feature of the product to be sold" includes:
step D1: determining the cooperative characteristics between the shopping behavior identification of the target user and the commodity to be sold according to the effective attribute characteristics and the shopping behavior characteristics of the commodity to be sold; the collaborative characteristics are used for expressing the association degree between the attribute characteristics of the target commodity corresponding to the shopping behavior identification and the effective attribute characteristics of the commodity to be sold.
Step D2: and determining the probability of the target user purchasing the commodity to be sold according to the cooperative characteristics.
In the embodiment of the invention, after the effective attribute characteristics and the shopping behavior characteristics are determined, the cooperation characteristics between each shopping behavior identification and the commodity to be sold are determined by taking the shopping behavior identification as a unit, namely, the association degree between the attribute characteristics of the target commodity corresponding to the shopping behavior identification and the effective attribute characteristics of the commodity to be sold is determined. Specifically, the degree of association between the attribute feature of the target product and the effective attribute feature of the product to be sold may be a degree of similarity between the two (i.e., the attribute feature of the target product and the effective attribute feature of the product to be sold), or may be a degree of correlation between the two; for example, if the target product and the product to be sold are similar products of the same brand and the same price, the similarity between the target product and the product to be sold is higher, and the corresponding association degree is also higher; or, if the target commodity is a mobile phone and the commodity to be sold is a mobile phone protection shell suitable for the mobile phone, the correlation degree between the target commodity and the commodity to be sold is high, and the corresponding correlation degree is also high.
According to the method for recommending the main body, provided by the embodiment of the invention, effective attribute characteristics of the commodity to be sold are extracted by carrying out reduction processing on commodity information; after determining the shopping behavior characteristics of the target user, whether the target user is likely to purchase the goods for sale is determined based on the effective attribute characteristics and the shopping behavior characteristics, and then whether the target subject is recommended to the target user can be determined. The method and the device have the advantages that the target subject is recommended to the target user based on the probability that the target user purchases the commodity for sale, and the subject which is more interested in the target subject can be recommended to the user; the effective attribute characteristics determined after reduction can accurately represent the characteristics of the commodity to be sold through a small amount of attribute characteristics, so that whether the target user is possible to purchase the commodity to be sold or not can be conveniently, quickly and accurately determined subsequently, the processing amount can be reduced, and the processing efficiency can be improved. The effective attribute characteristics of the commodities to be sold are determined through the effective attribute characteristics related to the target user, the possibility that the target user purchases the commodities to be sold can be better represented through the effective attribute characteristics, and the target main body can be more accurately recommended to the target user. In addition, because the number of users is generally much greater than that of the subjects, the effective attribute features of the commodities to be sold are determined based on the effective attribute features related to the target users in the embodiment, the target subjects can be allocated to the target users instead of the target subjects, the processing amount can be greatly reduced, and the subject recommendation can be realized as soon as possible. And judging based on the extracted collaborative features, the main body recommendation problem can be converted into a two-classification problem, and the problem can be simplified.
The method for recommending the subject according to the embodiment of the present invention is described above in detail, and the method can also be implemented by a corresponding apparatus.
Fig. 2 is a schematic structural diagram of an apparatus for recommending a subject according to an embodiment of the present invention. As shown in fig. 2, the apparatus for recommending a subject includes:
the first acquisition module 21 is configured to acquire commodity information of a commodity to be sold in a target main body, perform reduction processing on the commodity information, and extract effective attribute features of the commodity to be sold;
the second obtaining module 22 is configured to obtain shopping information of a target user, and extract a shopping behavior feature corresponding to a target commodity, where the shopping behavior feature includes a shopping behavior identifier and an attribute feature of the target commodity;
the processing module 23 is configured to determine, according to the effective attribute feature of the to-be-sold commodity and the shopping behavior feature, a probability that the target user purchases the to-be-sold commodity, and recommend the target subject to the target user when the probability satisfies a preset condition.
On the basis of the foregoing embodiment, the reducing processing performed by the first obtaining module 21 on the commodity information to extract the effective attribute feature of the commodity to be sold includes:
determining all undetermined attribute characteristics of the commodity to be sold, carrying out reduction processing on the undetermined attribute characteristics, and selecting a part from the undetermined attribute characteristics as an effective attribute characteristic of the commodity to be sold;
or, the reducing the commodity information to extract the effective attribute features of the commodity to be sold includes:
determining an attribute feature set of the target commodity corresponding to the shopping behavior feature of the target user, and taking a corresponding shopping behavior identifier in the shopping behavior feature as a decision feature; the attribute feature set comprises a plurality of original attribute features;
taking one original attribute feature as a target attribute feature, and if the conditional information entropy of the decision feature relative to the attribute feature set is increased when the attribute feature set lacks the target attribute feature, taking the target attribute feature as an effective attribute feature of the target commodity;
traversing all original attribute features in the attribute feature set, determining all effective attribute features of the target commodity, and taking the effective attribute features of the target commodity as the effective attribute features of the commodity to be sold.
On the basis of the above embodiment, the apparatus further includes: a subject audit module;
before the first obtaining module 21 obtains the commodity information of the commodity to be sold in the target subject, the subject auditing module is configured to:
and auditing the undetermined subject according to the sales information of the undetermined subject, and taking the undetermined subject as a target subject when the audit is passed.
On the basis of the above embodiment, the apparatus further includes: a user screening module;
before the second obtaining module 22 obtains the shopping information of the target user, the user filtering module is configured to:
determining keywords of the commodities to be sold according to commodity information of the commodities to be sold in the target main body, and taking a user with shopping behaviors in a preset time period and the keywords correlated as a target user.
On the basis of the above embodiment, the second obtaining module 22 obtains the shopping information of the target user, and extracts the shopping behavior characteristics corresponding to the target product, including:
the method comprises the steps of obtaining shopping information of a target user in a preset time period, carrying out reduction processing on the shopping information, and extracting corresponding shopping behavior characteristics, wherein the shopping behavior characteristics comprise shopping behavior identification and effective attribute characteristics of a reduced target commodity.
On the basis of the above embodiment, the determining, by the processing module 23, the probability that the target user purchases the commodity to be sold according to the effective attribute feature of the commodity to be sold and the shopping behavior feature includes:
determining the cooperative characteristics between the shopping behavior identification of the target user and the commodity to be sold according to the effective attribute characteristics of the commodity to be sold and the shopping behavior characteristics; the collaborative feature is used for representing the association degree between the attribute feature of the target commodity corresponding to the shopping behavior identifier and the effective attribute feature of the commodity to be sold;
and determining the probability of the target user purchasing the commodity to be sold according to the cooperative characteristics.
According to the device for recommending the main body, the effective attribute characteristics of the commodity to be sold are extracted by carrying out reduction processing on the commodity information; after determining the shopping behavior characteristics of the target user, whether the target user is likely to purchase the goods for sale is determined based on the effective attribute characteristics and the shopping behavior characteristics, and then whether the target subject is recommended to the target user can be determined. The method and the device have the advantages that the target subject is recommended to the target user based on the probability that the target user purchases the commodity for sale, and the subject which is more interested in the target subject can be recommended to the user; the effective attribute characteristics determined after reduction can accurately represent the characteristics of the commodity to be sold through a small amount of attribute characteristics, so that whether the target user is possible to purchase the commodity to be sold or not can be conveniently, quickly and accurately determined subsequently, the processing amount can be reduced, and the processing efficiency can be improved. The effective attribute characteristics of the commodities to be sold are determined through the effective attribute characteristics related to the target user, the possibility that the target user purchases the commodities to be sold can be better represented through the effective attribute characteristics, and the target main body can be more accurately recommended to the target user. In addition, because the number of users is generally much greater than that of the subjects, the effective attribute features of the commodities to be sold are determined based on the effective attribute features related to the target users in the embodiment, the target subjects can be allocated to the target users instead of the target subjects, the processing amount can be greatly reduced, and the subject recommendation can be realized as soon as possible. And judging based on the extracted collaborative features, the main body recommendation problem can be converted into a two-classification problem, and the problem can be simplified.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the method embodiment of the recommended subject are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Specifically, referring to fig. 3, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of the recommendation subject described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus, and memory controller, a peripheral bus, an Accelerated Graphics Port (AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include: an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA), a Peripheral Component Interconnect (PCI) bus.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet (intranet), an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and combinations of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced Mobile Broadband (eMBB) system, a mass Machine Type Communication (mtc) system, an ultra reliable Low Latency Communication (urrllc) system, or the like.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: Read-Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), or Flash Memory.
The volatile memory includes: random Access Memory (RAM), which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory (Static RAM, SRAM), Dynamic random access memory (Dynamic RAM, DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (Double Data RateSDRAM, DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media Player (Media Player), Browser (Browser), for implementing various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements each process of the method embodiment of the recommendation subject, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here.
The computer-readable storage medium includes: permanent and non-permanent, removable and non-removable media may be tangible devices that retain and store instructions for use by an instruction execution apparatus. The computer-readable storage medium includes: electronic memory devices, magnetic memory devices, optical memory devices, electromagnetic memory devices, semiconductor memory devices, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), non-volatile random access memory (NVRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic tape cartridge storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanically encoded devices (e.g., punched cards or raised structures in a groove having instructions recorded thereon), or any other non-transmission medium useful for storing information that may be accessed by a computing device. As defined in embodiments of the present invention, the computer-readable storage medium does not include transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses traveling through a fiber optic cable), or electrical signals transmitted through a wire.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, electronic device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the embodiment of the invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be substantially or partially contributed by the prior art, or all or part of the technical solutions may be embodied in a software product stored in a storage medium and including instructions for causing a computer device (including a personal computer, a server, a data center, or other network devices) to execute all or part of the steps of the methods of the embodiments of the present invention. And the storage medium includes various media that can store the program code as listed in the foregoing.
The above description is only a specific implementation of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present invention, and all such changes or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A method of recommending a subject, comprising:
auditing the undetermined subject according to the sales information of the undetermined subject, and taking the undetermined subject as a target subject when the auditing is passed;
acquiring commodity information of commodities to be sold in a target main body, carrying out reduction processing on the commodity information, and extracting effective attribute features of the commodities to be sold;
the method comprises the steps of obtaining shopping information of a target user, and extracting shopping behavior characteristics corresponding to a target commodity, wherein the shopping behavior characteristics comprise a shopping behavior identifier and attribute characteristics of the target commodity;
determining the probability of the target user for purchasing the commodity to be sold according to the effective attribute characteristics of the commodity to be sold and the shopping behavior characteristics, and recommending the target main body to the target user when the probability meets a preset condition;
the reducing processing is performed on the commodity information, and effective attribute features of the commodity to be sold are extracted, and the method comprises the following steps:
determining an attribute feature set of the target commodity corresponding to the shopping behavior feature of the target user, and taking a corresponding shopping behavior identifier in the shopping behavior feature as a decision feature; the attribute feature set comprises a plurality of original attribute features;
taking one original attribute feature as a target attribute feature, and if the conditional information entropy of the decision feature relative to the attribute feature set is increased when the attribute feature set lacks the target attribute feature, taking the target attribute feature as an effective attribute feature of the target commodity;
traversing all original attribute features in the attribute feature set, determining all effective attribute features of the target commodity, and taking the effective attribute features of the target commodity as the effective attribute features of the commodity to be sold.
2. The method according to claim 1, wherein the reducing the commodity information to extract the effective attribute feature of the commodity to be sold comprises:
determining all undetermined attribute characteristics of the commodity to be sold, reducing the undetermined attribute characteristics, and selecting a part from the undetermined attribute characteristics as an effective attribute characteristic of the commodity to be sold.
3. The method of claim 1, wherein prior to said obtaining shopping information for the target user, the method further comprises:
determining keywords of the commodities to be sold according to commodity information of the commodities to be sold in the target main body, and taking a user with shopping behaviors in a preset time period and the keywords correlated as a target user.
4. The method of claim 1, wherein the obtaining of the shopping information of the target user and the extracting of the shopping behavior characteristics corresponding to the target product comprises:
the method comprises the steps of obtaining shopping information of a target user in a preset time period, carrying out reduction processing on the shopping information, and extracting corresponding shopping behavior characteristics, wherein the shopping behavior characteristics comprise shopping behavior identification and effective attribute characteristics of a reduced target commodity.
5. The method according to any one of claims 1 to 4, wherein the determining the probability of the target user purchasing the commodity to be sold according to the effective attribute feature of the commodity to be sold and the shopping behavior feature comprises:
determining the cooperative characteristics between the shopping behavior identification of the target user and the commodity to be sold according to the effective attribute characteristics of the commodity to be sold and the shopping behavior characteristics; the collaborative feature is used for representing the association degree between the attribute feature of the target commodity corresponding to the shopping behavior identifier and the effective attribute feature of the commodity to be sold;
and determining the probability of the target user purchasing the commodity to be sold according to the cooperative characteristics.
6. An apparatus for recommending a subject, comprising:
the subject auditing module is used for auditing the undetermined subject according to the sales information of the undetermined subject, and taking the undetermined subject as a target subject when the auditing is passed;
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring commodity information of commodities to be sold in a target main body, reducing the commodity information and extracting effective attribute characteristics of the commodities to be sold;
the second acquisition module is used for acquiring the shopping information of a target user and extracting the shopping behavior characteristics corresponding to the target commodity, wherein the shopping behavior characteristics comprise a shopping behavior identifier and the attribute characteristics of the target commodity;
the processing module is used for determining the probability of the target user for purchasing the commodity to be sold according to the effective attribute characteristics of the commodity to be sold and the shopping behavior characteristics, and recommending the target main body to the target user when the probability meets preset conditions;
the reducing processing is performed on the commodity information, and effective attribute features of the commodity to be sold are extracted, and the method comprises the following steps:
determining an attribute feature set of the target commodity corresponding to the shopping behavior feature of the target user, and taking a corresponding shopping behavior identifier in the shopping behavior feature as a decision feature; the attribute feature set comprises a plurality of original attribute features;
taking one original attribute feature as a target attribute feature, and if the conditional information entropy of the decision feature relative to the attribute feature set is increased when the attribute feature set lacks the target attribute feature, taking the target attribute feature as an effective attribute feature of the target commodity;
traversing all original attribute features in the attribute feature set, determining all effective attribute features of the target commodity, and taking the effective attribute features of the target commodity as the effective attribute features of the commodity to be sold.
7. The apparatus according to claim 6, wherein the first obtaining module performs reduction processing on the commodity information to extract effective attribute features of the commodity to be sold, and the method includes:
determining all undetermined attribute characteristics of the commodity to be sold, reducing the undetermined attribute characteristics, and selecting a part from the undetermined attribute characteristics as an effective attribute characteristic of the commodity to be sold.
8. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps in the method of recommending subjects according to any of claims 1 to 5.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps in the method of recommending a subject according to any of claims 1 to 5.
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