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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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
From th...
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Method used

In summary, this method regards comments and ratings as the interactive features of users and products, adaptively learns the mutual influence between the two, and learns more accurate node representations and more delicate user preferences, thereby improving the recommendation performance
In the present embodiment, a kind of graph neural network recommendation method oriented to comment text considers that comment text modeling is not suitable for comment as the actual situation of interactive feature in the existing recommendation model based on comment, by convolution in graph Model users and products with reviews as edge features, and use reviews to calculate user and product representations more accurately. On this basis, graph contrastive learning is introduced to enhan...
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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.

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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

Examples

  • Experimental program(1)

Example Embodiment

[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 0.8006 1.108 0.8654 1.1837 TransNets 0.8273 0.798 1.1141 0.844 1.1855 GC-MC 0.809 0.7986 1.1088 0.8404 1.1737 RMG 0.8074 0.7901 1.1064 0.8425 1.1705 SSG 0.8218 0.8064 1.1228 0.8458 1.1807 RGCL 0.7735 0.7771 1.0858 0.818 1.1609
[0107] Table 1 is a comparison of the recommended effects between the method of the present invention and the comparison method. It can be seen from the experimental results that the method proposed by the present invention is superior to the existing method in MSE index.
[0108] In summary, this method regards reviews and ratings as the interaction features between users and products, adaptively learns the interaction between the two, and learns more accurate node representations and more delicate user preferences, thereby improving the recommendation performance.
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