Comment text-oriented graph neural network recommendation method

A neural network and recommendation method technology, applied in the field of designing personalized recommendation, can solve the problems that node representation is difficult to reflect user preferences and product characteristics, limit recommendation effect, ignore user-product interaction, etc., so as to improve recommendation performance and optimize model parameters. , the effect of accurate recommendation accuracy

Pending Publication Date: 2022-07-08
HEFEI UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Although existing review-based recommendation methods achieve more accurate recommendation results than traditional recommendation models, these methods ignore that user-product interaction can be modeled naturally with a bipartite graph: users and products are nodes, ratings and review text Reflects the characteristics of the edges between nodes
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  • Comment text-oriented graph neural network recommendation method
  • Comment text-oriented graph neural network recommendation method
  • Comment text-oriented graph neural network recommendation method

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[0103] Experimental example:

[0104] In order to verify the effectiveness of this method, the present invention conducts experiments on five public datasets commonly used in recommender systems: DigitalMusic, Toys and Games, Clothing, CDs and Yelp, and compares the method of the present invention (RGCL) with 10 existing recommendations Method: Recommendation performance of SVD, NCF, DeepCONN, NARRE, DAML, SDNet, TransNet, GC-MC, RMG and SSG. The evaluation index adopts MSE commonly used in recommender systems. The smaller the MSE, the smaller the error of score prediction is. higher precision.

[0105] Table 1

[0106] Recommended method Digital Music Toys and Games Clothing CDs Yelp SVD 0.8523 0.8086 1.1167 0.8662 1.1939 NCF 0.8403 0.8078 1.1094 0.8781 1.1896 DeepCoNN 0.8378 0.8028 1.1184 0.8621 1.1877 NARRE 0.8172 0.7962 1.1064 0.8495 1.1862 DAML 0.8237 0.7936 1.1065 0.8483 1.1793 SDNet 0.8331 0.8...

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Abstract

The invention discloses a comment text-oriented graph neural network recommendation method, which comprises the following steps of: 1, constructing a bipartite graph of a user and a commodity by using comments and scores of the user on the product, including a user node set, a product node set, a score matrix and a comment feature tensor; 2, constructing a comment text-oriented graph convolution method, taking comments and scores as edge features to participate in graph convolution, and coding user and product characterization; and 3, enhancing the characterization of the user and the product by utilizing graph contrast learning. 4, constructing an interaction layer, and coding an interaction vector from the representation of the user and the product; and 5, drawing close the distribution of the interactive representation and the comment vector through comparative learning. And 6, predicting a score according to the interactive characterization so as to realize product recommendation. According to the method, comments and scores are used as interaction features of the user and the product, the mutual influence between the comments and the scores is adaptively learned, more accurate node characterization and finer user preference are learned, and therefore the recommendation performance can be improved.

Description

technical field [0001] The present invention is designed in the field of personalized recommendation, specifically, a graph neural network recommendation method oriented to comment text. Background technique [0002] With the rapid development of Internet technology, a large amount of information has emerged in the network service platform for users to choose, and the problem of information overload has become increasingly prominent. Personalized recommendation system recommends products that users are interested in by analyzing user's historical behavior and mining user's interest preferences. A large amount of user comment information in e-commerce websites can help to further explore user preferences and improve the recommendation effect. Current mainstream methods using review text treat historical reviews as descriptive texts of users and products, and extract semantic features from them to enhance the representation of users and products. [0003] Although existing r...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06Q10/06G06Q10/04G06N3/04G06N3/08
CPCG06Q30/0631G06Q10/04G06Q10/06393G06N3/08G06N3/048G06N3/045
Inventor 张琨帅杰吴乐洪日昌汪萌
Owner HEFEI UNIV OF TECH
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