Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation

Xin, X., He, X., Zhang, Y., Zhang, Y. and Jose, J. (2019) Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. In: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19), Paris, France, 21-25 Jul 2019, pp. 125-134. ISBN 9781450361729 (doi: 10.1145/3331184.3331188)

Abstract

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world scenarios, e.g., two movies share the same director, two products complement with each other, etc. Distinct from the collaborative similarity that implies co-interact patterns from the user's 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) to exploit multiple item relations in recommender systems. We find that both the relation type (e.g., shared director) and the relation value (e.g., Steven Spielberg) 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. 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 user preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF1. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by RCF's modeling of multiple item relations.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Xin, Xin
• Authors: Xin, X., He, X., Zhang, Y., Zhang, Y., and Jose, J.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450361729
• Copyright Holders: Copyright © 2019 Association for Computing Machinery
• First Published: First published in SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
• Publisher Policy: Reproduced in accordance with the publisher copyright policy