Localized Graph Collaborative Filtering

TL;DR

This paper introduces LGCF, a graph neural network-based collaborative filtering method that encodes local graph information for recommendations, especially effective in sparse data scenarios, and enhances existing embedding-based models.

Contribution

LGCF offers a novel approach that avoids learning individual user and item embeddings, focusing instead on localized graph encoding for improved sparse recommendation performance.

Findings

LGCF outperforms traditional GNN-based methods in sparse scenarios.

LGCF provides complementary information to embedding-based models.

Extensive experiments validate LGCF's effectiveness across datasets.

Abstract

User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well wit sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance.