Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems

Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive inter...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Gu, Hongjian, Hu, Yaochen, Zhang, Yingxue
Format: Article
Language:English
Subjects:
Bayesian analysis
Graph neural networks
Graphs
Recommender systems
System effectiveness
Online Access:Get full text
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Summary:Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios. The Bayesian Graph Neural Network framework approaches this issue with generative models for the interaction graphs. The critical problem is to devise a proper family of graph generative models tailored to recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information. Experiments on four popular benchmark datasets demonstrate the effectiveness of our proposed graph generative methods for recommender systems.
ISSN:2331-8422