A Graph Neural Network Recommendation Method Incorporating Review Information

A neural network and recommendation method technology, applied in the field of computer information recommendation, can solve problems such as inability to make good use of other useful information, inability to identify user concerns, and single interactive information, and achieve the effect of contributing to structural representation

Active Publication Date: 2022-05-03
ANHUI AGRICULTURAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (1) Sensitive to data sparsity and cold start
[0006] (2) Single use of interaction information between users and items cannot make good use of other useful information
[0007] (3) For high-order neighbors, the user's concerns cannot be well identified

Method used

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  • A Graph Neural Network Recommendation Method Incorporating Review Information
  • A Graph Neural Network Recommendation Method Incorporating Review Information
  • A Graph Neural Network Recommendation Method Incorporating Review Information

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Embodiment Construction

[0075] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0076] like figure 1 As shown, the present invention is a graph neural network recommendation method that incorporates comment information, uses the BERT model to extract the content representations of users and items from user comment texts, and then constructs bipartite graphs of users and items, and uses graph neural networks to learn from user — Learn the structural representations of users and items in the item bipartite graph, and fuse them with content representations as the final embedded representations of users and items. Finally, the multi-layer perceptron is used to predict the user's interaction probability for each item, and the items are sorted according to the user's predicted interaction probability for each item, and the items with higher probability are selected to generate the Top-N recommendation list.

[0077] The present inventio...

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Abstract

The invention discloses a graph neural network recommendation method incorporating comment information, using user comment text as a source of node information, and using a BERT model to perform feature extraction of text data to obtain a content expression vector of each node. Then build a user-item bipartite graph based on the interaction between the user and the item, and learn the structural representation of the user and the item by building a graph neural network on the bipartite graph to aggregate the first-order and third-order neighbor information of the nodes. Representations are fused with content representations as the final embedded representations of users and items. Finally, the user's interaction probability for each item is predicted by the multi-layer perceptron MLP. For the obtained prediction results, use Top-N sorting to generate a list of recommended items. The invention can more accurately capture user preference, find user's point of interest, and perform more accurate and effective recommendation on it.

Description

technical field [0001] The invention relates to the field of computer information recommendation methods, in particular to a graph neural network recommendation method incorporating comment information. Background technique [0002] The recommendation system can solve the problem of information overload and develop rapidly. For example, the software on the mobile phone, shopping, music, video, etc. are all related to recommendation. Most of the core is to analyze the user's behavior to study the user's interests and hobbies, and recommend the user's interest from a large amount of data. [0003] Traditional recommendation algorithms include content-based recommendation, collaborative filtering-based recommendation and hybrid recommendation. Content-based recommendation uses information comparison and filtering technology. The content of the item does not need to be scored, but it needs easy-to-extract content features and strong structure. The recommendation accuracy is not...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F40/284G06F40/30G06N3/04G06N3/08
CPCG06F16/9535G06N3/084G06F40/284G06F40/30G06N3/047G06N3/045
Inventor 吴国栋王伟娜李景霞涂立静刘玉良
Owner ANHUI AGRICULTURAL UNIVERSITY
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