Disentangled Graph Collaborative Filtering

Abstract

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering (DGCF), to disentangle these factors and yield disentangled representations. Specifically, by modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. Meanwhile, we encourage independence of different intents. This leads to disentangled representations, effectively distilling information pertinent to each intent. We conduct extensive experiments on three benchmark datasets, and DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE. Further analyses offer insights into the advantages of DGCF on the disentanglement of user intents and interpretability of representations. Our codes are available in https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering.

SIGIR 2020

References in corpus (1)

• KGAT: Knowledge Graph Attention Network for Recommendation

Cited by in corpus (12)

• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
• Hypergraph Contrastive Collaborative Filtering
• Towards Representation Alignment and Uniformity in Collaborative Filtering
• Deconfounded Recommendation for Alleviating Bias Amplification
• Self-Supervised Hypergraph Transformer for Recommender Systems
• Disentangling Long and Short-Term Interests for Recommendation
• DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
• Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
• MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
• Revisiting Neighborhood-based Link Prediction for Collaborative Filtering
• Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems