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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  • Abstract
  • Description
  • Claims
  • Application Information

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 technolo

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

Examples

Experimental program
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Example Embodiment

[0076] Example 1

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

[0078] S1: Collect the data set and perform the data set, divide the data field processing, the domain C is the target domain, the domain B, the field M, and the domain E is an aided domain;

[0079] S2: Multiple auxiliary domains are used with Dctor models based on self-focus mechanism, namely the SA-Dctor model, and training, respectively, and obtain common user information matrices in the corresponding field and target domain, respectively.

[0080] S3: The plurality of common user information matrix inputs the self-focus mechanism module and splicing the fused vector to obtain a fusion matrix of score information and text information after multi-domain fusion;

[0081] S4: Enter the fusion matrix into a multi-layer perceived machine for nonlinear relationships between users and items;

[0082] S5: Enter the outpu...

Example Embodiment

[0086] Example 2

[0087] More specifically, for a common user information extraction section of a single model, use self-focus mechanism instead of the original poolization technology, the principle of self-focus mechanism image 3 As shown, Query and Key are hidden states of decoders and encoders, and Value is an embedding vector to extract information. Query and Key have generated corresponding weight A after a cost of calculating similarity, normalization, mask, and SoftMax. The obtained weight A is multiplied by the information vector value, which gives weights for each input vector according to the similarity. It will make it unforeseen or special. (Specifically, using max-pooling will remain in two fields, that is, the specialty but loss of generality. Use Average-Pool, simply retain the mean of both, that is, the general but lost specialty.)

[0088] More specifically, the DCTOR model (hereinafter referred to as the SA-Dctor Model) is based on self-focus mechanism. Figure 4...

Example Embodiment

[0146] Example 3

[0147] More specifically, the present invention is based on the integrated model of integrated model, and proposes a multi-auxiliary domain information fusion model based on self-focus mechanism and DCTOR, such as the N-SA-DCTOR model. Figure 10 Indicated. Assuming that the domain C is the target domain, the domain B, the field M, and the domain E are allocated. The first step, first use the auxiliary domain to use the Dctor model (hereinafter referred to as the SA-Dctor model) based on the self-focus mechanism, respectively, and generate a common user information vector D of the primary domain and each auxiliary domain, respectively. mc , D bc And D ec . In the second step, the plurality of common users are input to a layer of self-focus mechanism for weighting fusion, and the fused vector is condensed to obtain a multi-domain fusion vector U. The third step uses a fully connected multi-layer perception (MLP) to learn the nonlinear relationship between the use...

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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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