Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems
This work addresses data quality issues in industrial recommender systems, but it is incremental as it builds on existing Bayesian GNN frameworks.
The paper tackled the problem of missing or spurious interactions in graph-based recommender systems by proposing a Bayesian Graph Neural Network with a generative model that incorporates user preferences, item concurrence, and graph structure, showing effectiveness on four benchmark datasets.
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.