DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation

作     者：Li, Hourun Wang, Yifan Xiao, Zhiping Yang, Jia Zhou, Changling Zhang, Ming Ju, Wei 

作者机构：State Key Laboratory for Multimedia Information Processing School of Computer Science PKU-Anker LLM Lab Peking University Beijing China Computer Center Peking University Beijing China School of Information Technology & Management University of International Business and Economics Beijing China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States College of Computer Science Sichuan University Chengdu China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Contrastive Learning 

摘      要：Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains. Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components. Copyright © 2024, The Authors. All rights reserved.