A product recommendation method based on multi-modal product feature fusion
A product recommendation and feature fusion technology, applied in specific mathematical models, neural learning methods, business, etc., can solve problems such as different information, multi-noise information, etc.
Active Publication Date: 2022-05-06
GUILIN UNIV OF ELECTRONIC TECH
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However, comment information is given by users. Due to different user expression habits and concerns, the information contained in different comments usually has different information, and may even contain a lot of noise information.
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[0040] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.
[0041] like figure 1 As shown, the present invention provides a product recommendation method based on multimodal product feature fusion, including the following steps:
[0042] Step 1: Construct a user-product bipartite graph according to the product sequence purchased by the user in history, and obtain the vector representation of the user node and the vector representation of the product node through graph convolution;
[0043] Specifically, the specific steps of graph convolution are:
[0044] First, construct a user-item bipartite graph based on the historical interaction between users and items, and use the user-item adjacency matrix Indicates that n u and n p are the number of users...
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The invention belongs to the field of commodity recommendation, and in particular relates to a commodity recommendation method based on fusion of multimodal commodity features. The product recommendation method includes: constructing a user-commodity bipartite graph according to the product sequence that the user has purchased, obtaining the vector representation of the user node and the vector representation of the product node through graph convolution; Feature extraction to obtain the vector representation of product reviews; feature extraction of product title and description information through a convolutional neural network to obtain a vector representation of product content; connect product nodes, reviews and content vector representations to obtain the final product representation, Take the vector representation of the user node as the final representation of the user. The present invention can greatly alleviate the problem of data sparsity in product recommendation by utilizing the multi-modal features of the product, and improve the accuracy of recommendation.
Description
technical field [0001] The invention relates to a commodity recommendation method, which belongs to the field of commodity recommendation. Background technique [0002] In the process of modeling the product, most of the current product recommendation methods only use the id of the product to extract the synergy signal implicit in the interaction between the user and the product to model the product, which usually faces serious data sparsity However, this problem greatly restricts the performance of the recommendation system. Although there are also some works that take review information into consideration to capture item feature information contained in reviews while alleviating the data sparsity problem, the title and description information of the item itself is rarely exploited. However, comment information is given by users. Due to the different expression habits and concerns of users, the information contained in different comments usually has different information, ...
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IPC IPC(8): G06Q30/06G06F16/9535G06F40/289G06F40/216G06N3/04G06N3/08G06N7/00
CPCG06Q30/0631G06F16/9535G06F40/289G06F40/216G06N3/08G06N7/01G06N3/045
Inventor 蔡国永宋亚飞
Owner GUILIN UNIV OF ELECTRONIC TECH