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Multi-auxiliary-domain information fusion cross-domain recommendation method and system

A recommendation method and auxiliary domain technology, applied in the direction of neural learning methods, digital data information retrieval, biological neural network models, etc., can solve the problems of easy loss of part of feature information, recommendation, etc., to alleviate data sparsity, performance optimization, recommendation list complete effect

Pending Publication Date: 2021-03-09
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] In order to overcome the technical defects that the existing cross-domain recommendation algorithm cannot make recommendations in multiple domains, and when using pooling technology to extract information, it is easy to lose part of the feature information, and provides a multi-auxiliary domain information Integrating cross-domain recommendation method and system

Method used

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  • Multi-auxiliary-domain information fusion cross-domain recommendation method and system
  • Multi-auxiliary-domain information fusion cross-domain recommendation method and system
  • Multi-auxiliary-domain information fusion cross-domain recommendation method and system

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Experimental program
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Embodiment 1

[0077] Such as figure 2 As shown, a multi-assisted domain information fusion cross-domain recommendation method includes the following steps:

[0078] S1: Collect data sets and preprocess the data sets, and process the data in different domains. Set domain C as the target domain, domain B, domain M, and domain E as auxiliary domains;

[0079] S2: Multiple auxiliary domains are trained with the target domain using the DCTOR model based on the self-attention mechanism, that is, the SA-DCTOR model, to obtain the common user information matrix of the corresponding domain and the target domain;

[0080] S3: Input multiple common user information matrices into the self-attention mechanism module for feature fusion, and splicing the fused vectors to obtain a fusion matrix containing rating information and text information after multi-domain fusion;

[0081] S4: Input the fusion matrix into the multi-layer perceptron to learn the nonlinear relationship between users and items;

[0...

Embodiment 2

[0087] More specifically, for the common user information extraction part of a single model, the self-attention mechanism is used instead of the original pooling technology. The principle of the self-attention mechanism is as follows: image 3As shown, Query and Key are the hidden states of the decoder and encoder respectively, and Value is the Embedding vector to extract information. After Query and Key are calculated similarity by dot product, normalized, masked, and softmaxed, the corresponding weight a is generated. By multiplying the obtained weight a by the information vector Value, each input vector can be weighted according to the similarity. When using pooling technology, it will lose its generality or specificity. (Specifically, using max-pooling preserves the more prominent of the two domains, i.e. retaining specificity but losing generality. Using average-pooling simply preserves the mean of the two, i.e. retaining generality but losing specificity.)

[0088] Mor...

Embodiment 3

[0147] More specifically, based on the idea of ​​an integrated model, the present invention proposes a multi-auxiliary domain information fusion model (hereinafter referred to as the N-SA-DCTOR model) based on a self-attention mechanism and DCTOR, such as Figure 10 shown. Assume domain C is the target domain, domain B, domain M, and domain E are auxiliary domains. The first step is to use the DCTOR model based on the self-attention mechanism (hereinafter referred to as the SA-DCTOR model) for each auxiliary domain and C respectively, and generate the common user information vector D of the main domain and each auxiliary domain respectively. mc 、D bc and D ec . In the second step, multiple common user vectors are input to a layer of self-attention mechanism Layer for weighted fusion, and the fused vectors are concat spliced ​​to obtain a multi-domain fusion vector U. The third step uses a fully connected multi-layer perceptron (MLP) to learn the non-linear relationship bet...

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Abstract

According to the multi-auxiliary-domain information fusion cross-domain recommendation method provided by the invention, a self-attention mechanism is introduced to automatically calculate the corresponding weight for each common user (article) information vector, so that the problem of deviation or overlarge variance caused by a pooling technology is avoided, and the information of each vector isfused in a user (article) space. Meanwhile, the performance of the model applied to large-scale data is optimized through parallel calculation of a self-attention mechanism. According to the multi-auxiliary-domain fusion method, the bottleneck that an existing DCTOR model can only be applied to double-target recommendation is broken through, possibility is provided for multi-domain cross-domain recommendation, the diversity of recommended articles can be improved, a recommendation list is more complete, and an information cocoon house is prevented from being formed.

Description

technical field [0001] The present invention relates to the technical field of cross-domain recommendation, and more specifically, to a cross-domain recommendation method and system for information fusion of multiple auxiliary domains. Background technique [0002] With the rapid development of the Internet and the rapid growth of information resources on the Internet, the problem of "information overload" has become more and more serious. It has become very difficult for users to find information that meets their individual needs from a large number of resources such as texts, videos, images, and commodities. Personalized recommendation system is one of the key technologies to solve the above problems. The recommendation system can statistically analyze the user's interest preferences through the research of the user's historical behavior data, so as to guide the user to discover their own information needs and realize personalized recommendation. For users, the recommenda...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/33G06F40/216G06Q30/06G06N3/04G06N3/08
CPCG06F16/9535G06F16/3346G06F40/216G06Q30/0631G06N3/08G06N3/047G06N3/045
Inventor 廖永李卫军
Owner GUANGDONG UNIV OF TECH
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