Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Abstract

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations.

References in corpus (9)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Recurrent Models of Visual Attention
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• NAIS: Neural Attentive Item Similarity Model for Recommendation
• Learning over Knowledge-Base Embeddings for Recommendation
• Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
• Translation-based Recommendation
• Adversarial Personalized Ranking for Recommendation

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• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
• Multi-Behavior Graph Neural Networks for Recommender System
• Recent Advances in Heterogeneous Relation Learning for Recommendation
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack