On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
This work addresses the need for better recommendation accuracy and efficiency in graph-based collaborative filtering, particularly for cold-start users, though it appears incremental in its approach.
The paper tackled the problem of understanding and utilizing different signal components in graph collaborative filtering for recommender systems, resulting in a method that achieved up to 27.06% performance gain on a dataset and improved handling of sparse data.
Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartite graph for better recommendations. Specifically, recent studies show the success of graph neural networks (GNN) for CF is attributed to its low-pass filtering effects. However, current researches lack a study of how different signal components contributes to recommendations, and how to design strategies to properly use them well. To this end, from the view of spectral transformation, we analyze the important factors that a graph filter should consider to achieve better performance. Based on the discoveries, we design JGCF, an efficient and effective method for CF based on Jacobi polynomial bases and frequency decomposition strategies. Extensive experiments on four widely used public datasets show the effectiveness and efficiency of the proposed methods, which brings at most 27.06% performance gain on Alibaba-iFashion. Besides, the experimental results also show that JGCF is better at handling sparse datasets, which shows potential in making recommendations for cold-start users.