CN114936907A - Commodity recommendation method and system based on node type interaction - Google Patents
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Abstract
The invention discloses a commodity recommendation method and system based on node type interaction, which comprises the following steps: constructing a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and the user behavior as sides; after carrying out feature conversion on different types of nodes, mapping all the nodes to the same feature space; constructing type interaction functions among users, commodities, users, stores and commodity stores so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and endowing the nodes after the type interaction according to the edge types; and aggregating neighbor node information of the weighted nodes to update the heterogeneous information network, and recommending the commodities by adopting the updated heterogeneous information network according to the commodity recommending task. The method is not limited to a recommendation method based on the meta-path, and the feature conversion and interaction functions are designed aiming at the node type, so that the acquired network information is more comprehensive, and the problems of data sparseness and cold start are solved.
Description
Technical Field
The invention relates to the technical field of recommendation systems, in particular to a commodity recommendation method and system based on node type interaction.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The recommendation system deeply excavates the potential requirements of the user by learning the user preference, predicts the articles in which the user is interested and carries out personalized recommendation for the user. On the internet, people can browse and shop articles meeting the requirements of the people from various articles, but with the arrival of the big data era, the information amount is increased explosively, large-volume and various types of data are mixed together, on one hand, more purchasing choices are provided for users, on the other hand, the difficulty of selecting articles and filtering information by the users is also improved, therefore, the commodity recommending method and system aim to analyze different requirements of each user, personalized selecting and recommending are performed for the users from various articles, the information overload problem is relieved, and the shopping experience of the users is improved.
A commodity-based recommendation system belongs to a heterogeneous information network and comprises various types of nodes and various types of connecting edges. For example, the node includes an item, a user, etc., and the connecting edge includes a click, a purchase, an attention, etc. The nodes are analyzed according to different visual angles and have different attribute information, the articles can have attribute information such as clothes, food, skin care products and the like or attribute information such as essential goods and non-essential goods and the like, the users can have attribute information such as teenagers, middle-aged and old people or attribute information such as students and office workers, and the stores can have attribute information such as men's clothing, women's clothing and the like.
The basic steps of commodity recommendation are that user-commodity interaction data and all auxiliary information are modeled into a heterogeneous information network in a unified mode, and then network information is extracted to design a proper recommendation model. The recommendation based on the heterogeneous information network usually uses a manually designed meta-path to extract network information, but the method has high complexity, and because the heterogeneous information network contains rich semantic information, a plurality of meta-paths are difficult to exhaust, and the problem of information loss is easily caused in the process of searching for the neighbor node of the target vertex by using the meta-path.
Disclosure of Invention
In order to solve the problems, the invention provides a commodity recommendation method and system based on node type interaction, which aim to improve the comprehensiveness and accuracy of information acquired by a recommendation system, is not limited to a recommendation method based on a meta-path, designs a characteristic conversion function and an interaction function aiming at a node type, enables the acquired network information to be more comprehensive, and alleviates the problems of data sparseness and cold start.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a commodity recommendation method based on node type interaction, including:
constructing a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and the user behavior as sides;
after carrying out feature conversion on different types of nodes, mapping all the nodes to the same feature space;
constructing type interaction functions between users and commodities, users and stores and between commodities and stores so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and endowing the nodes after the type interaction according to edge types;
and aggregating neighbor node information of the weighted nodes to update the heterogeneous information network, and recommending the commodities by adopting the updated heterogeneous information network according to the commodity recommending task.
As an alternative embodiment, each type of node designs a feature transfer function
Such that each node maps to a d-dimensional vector; feature of node i after feature conversion
Comprises the following steps:
wherein h is i Is the initial characteristic of node i.
As an alternative embodiment, the user-commodity type interaction function H (a) u ,a g ) Comprises the following steps:
user-shop type interaction function H (a) u ,a s ) Comprises the following steps:
commodity-shop type interaction function H (a) g ,a s ) Comprises the following steps:
wherein k is j 、k i Respectively representing two different types of node features that perform type interaction.
As an alternative embodiment, the node characteristics after type interaction include:
for the node i of the user type, the characteristic h after type interaction i ' (u) is expressed as:
for the node i of the commodity type, the feature h after the type interaction i ' (g) is expressed as:
for node i of store type, feature h after type interaction i '(s) is expressed as:
wherein, the first and the second end of the pipe are connected with each other,
and the node i is subjected to feature conversion.
As an alternative embodiment, the characteristic h of the weighted node i "is represented as:
wherein h is i ' is a node feature after a type interaction,
is the normalized weight.
As an alternative embodiment, the weights are:
where W is the weight matrix and b is the offset vector.
As an alternative embodiment, the feature after aggregating neighbor node information
Expressed as:
wherein h is i "is a feature of the node after the entitlement,
is the feature of node i after feature conversion, H (a) i ,a j ) Is of type a i And type a j Type of (2) interaction function.
In a second aspect, the present invention provides a commodity recommendation system based on node type interaction, including:
the network construction module is configured to construct a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and taking the user behavior as a side;
the characteristic conversion module is configured to map all the nodes to the same characteristic space after carrying out characteristic conversion on the nodes of different types;
the type interaction module is configured to construct type interaction functions among users, commodities, users, stores and commodities, so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and to give weights to the nodes after the type interaction according to the edge types;
and the information aggregation module is configured to aggregate neighbor node information of the weighted nodes so as to update the heterogeneous information network, and the updated heterogeneous information network is adopted to recommend the commodities according to the commodity recommendation task.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a commodity recommendation method and system based on node type interaction, which aim at node type design type interaction functions, so that the acquired network information is more comprehensive, the problems of data sparseness and cold start are solved, and the recommendation result is interpretable and traceable.
Big data contains rich semantic information, and extracting semantic information through meta-paths generally selects meta-paths with rich connection relations and strong semantic characteristics, but finding such meta-paths requires more domain knowledge. The invention gets rid of the dependence on the meta-path, avoids the complexity of manually designing the meta-path and extracts more comprehensive network information for representation.
The commodity recommendation method and system based on node type interaction provided by the invention are focused on extracting node information, and learn rich semantic relations for recommendation tasks by keeping the structural characteristics of a network mode.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a commodity recommendation method based on node type interaction according to embodiment 1 of the present invention;
fig. 2 is a diagram of a heterogeneous information network according to embodiment 1 of the present invention.
Detailed Description
The invention is further explained by the following embodiments in conjunction with the drawings.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
The embodiment provides a commodity recommendation method based on node type interaction, and aims to improve comprehensiveness and accuracy of information acquisition of a recommendation system, and design an interaction function for a node type regardless of a recommendation method based on a meta path, so that acquired network information is more comprehensive, and the problems of data sparseness and cold start are solved.
As shown in fig. 1, the method specifically includes:
constructing a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and the user behavior as sides;
after carrying out feature conversion on different types of nodes, mapping all the nodes to the same feature space;
constructing type interaction functions among users, commodities, users, stores and commodity stores so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and endowing the nodes after the type interaction according to the edge types;
and aggregating neighbor node information of the weighted nodes to update the heterogeneous information network, and recommending the commodities by adopting the updated heterogeneous information network according to the commodity recommending task.
In this embodiment, the types of goods include food, makeup, apparel, stationery, digital code, etc., the users include young and male, the stores correspond to the types of goods, the user behavior includes purchase, collection, shelving, attention, etc., and the heterogeneous information network is constructed as shown in fig. 2.
Heterogeneous information network is represented as
Wherein
Representing a node set, and epsilon representing an edge set;
let node type mapping function and connection edge type mapping function be
Wherein
A set of node types is represented that is,
representing a set of connected edge types; for twoNeighboring node
Let its node type be a i 、a j The connecting edge is (i, j, r) i,j ) E is epsilon, and the type of connecting edge is r i,j 。
It can be understood that all the data acquisition is carried out on the basis of compliance with laws and regulations and user consent, and the data is legally applied.
In the embodiment, a characteristic conversion function C is designed for different types of nodes of commodity types, shops and user types; due to the complex recommended data types and different feature spaces of different types of nodes, setting a graph
The initial characteristics of the middle nodes i and j are h respectively i 、h j First, the node types are classified into
Designing a feature transfer function for each type of node
So that
Mapping all types of nodes into the same feature space, namely mapping each node to a d-dimensional vector;
specifically, the feature of the node i, j after feature conversion is expressed as:
in the present embodiment, user-item, user-store, and item-store are constructed separatelyInter-shop type interaction function H (a) u ,a g )、H(a u ,a s ) And H (a) g ,a s ) To learn the correlation and conversion relationship among users, commodities and shops, respectively;
in particular, the type interaction function H (a) between the user and the commodity node u ,a g ) Comprises the following steps:
type interaction function H (a) between user and shop nodes u ,a s ) Comprises the following steps:
commodity-store node type interaction function H (a) g ,a s ) Comprises the following steps:
wherein k is j 、k i Respectively representing two different types of node features that perform type interaction.
For the node i belonging to the user type, the characteristics after interaction are expressed as follows:
for the node i belonging to the commodity type, the characteristics after interaction are expressed as follows:
for a node i belonging to a store type, the interacted characteristics are represented as:
in this embodiment, the nodes after type interaction are weighted according to edge types; namely:
wherein the content of the first and second substances,
is the normalized weight;
where W is the weight matrix and b is the offset vector.
In this embodiment, aggregation of neighbor node information is performed on the weighted node to obtain high-order semantic information of the node. The neighbor nodes are nodes connected with the node i, node information connected with the node i is aggregated, and a self-loop is added for each node, so that the nodes retain own characteristics, extract richer attribute information of the network, and simultaneously have a certain relieving effect on the cold start problem.
The characteristics after aggregating the neighbor node information are expressed as:
in this embodiment, according to the processing of the nodes, the heterogeneous information network is updated, and optimization is performed through a cross entropy loss function, so that after the commodities are sequenced according to the commodity recommendation task, recommendation is performed in combination with a top-k recommendation strategy.
Example 2
The embodiment provides a commodity recommendation system based on node type interaction, which comprises:
the network construction module is configured to construct a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and taking the user behavior as a side;
the characteristic conversion module is configured to map all the nodes to the same characteristic space after carrying out characteristic conversion on the nodes of different types;
the type interaction module is configured to construct type interaction functions among users, commodities, users, stores and commodities, so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and to endow the nodes after the type interaction according to the edge types;
and the information aggregation module is configured to aggregate neighbor node information of the weighted nodes so as to update the heterogeneous information network, and the updated heterogeneous information network is adopted to recommend the commodities according to the commodity recommendation task.
It should be noted that the modules correspond to the steps described in embodiment 1, and the modules are the same as the corresponding steps in the implementation examples and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, further description is omitted herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (10)
1. A commodity recommendation method based on node type interaction is characterized by comprising the following steps:
constructing a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and taking the user behavior as a side;
after carrying out feature conversion on different types of nodes, mapping all the nodes to the same feature space;
constructing type interaction functions among users, commodities, users, stores and commodity stores so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and endowing the nodes after the type interaction according to the edge types;
and aggregating neighbor node information of the weighted nodes to update the heterogeneous information network, and recommending the commodities by adopting the updated heterogeneous information network according to the commodity recommending task.
2. The commodity recommendation method based on node type interaction as claimed in claim 1, wherein each type of node designs a feature transformation function
Such that each node maps to a d-dimensional vector; feature of node i after feature conversion
Comprises the following steps:
wherein h is i Is the initial characteristic of node i.
3. The commodity recommendation method based on node type interaction as claimed in claim 1, wherein a user-commodity type interaction function H (a) u ,a g ) Comprises the following steps:
user-storeInter-shop type interaction function H (a) u ,a s ) Comprises the following steps:
commodity-shop type interaction function H (a) g ,a s ) Comprises the following steps:
wherein k is j 、k i Respectively representing two different types of node characteristics for type interaction.
4. The commodity recommendation method based on node type interaction as claimed in claim 1, wherein the node characteristics after type interaction include:
for the node i of the user type, the characteristic h after type interaction i ' (u) is expressed as:
for the node i of the commodity type, the feature h after the type interaction i ' (g) is expressed as:
for node i of store type, feature h after type interaction i '(s) is expressed as:
wherein the content of the first and second substances,
and the node i is subjected to feature conversion.
5. The commodity recommendation method based on node type interaction as claimed in claim 1, wherein the feature h of the weighted node i "is represented as: h is i ″＝(β ri +1)h i '; wherein h is i ' is a node feature after type interaction, beta ri Is the normalized weight.
6. The commodity recommendation method based on node type interaction as claimed in claim 5, wherein the weight is:
where W is the weight matrix and b is the offset vector.
7. The commodity recommendation method based on node type interaction as claimed in claim 1, wherein the characteristics after aggregating neighbor node information
Expressed as:
wherein h is i "is a feature of the node after the entitlement,
is the feature of node i after feature conversion, H (a) i ,a j ) Is of type a i And type a j Type of (2) interaction function.
8. A commodity recommendation system based on node type interaction is characterized by comprising:
the network construction module is configured to construct a heterogeneous information network by taking the commodity type, the shop and the user type as nodes and taking the user behavior as a side;
the characteristic conversion module is configured to map all the nodes to the same characteristic space after carrying out characteristic conversion on the nodes of different types;
the type interaction module is configured to construct type interaction functions among users, commodities, users, stores and commodities, so as to carry out different types of type interaction on the node characteristics after the characteristic conversion, and to endow the nodes after the type interaction according to the edge types;
and the information aggregation module is configured to aggregate neighbor node information of the weighted nodes so as to update the heterogeneous information network, and the updated heterogeneous information network is adopted to recommend the commodities according to the commodity recommendation task.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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