MP2: a momentum contrast approach for recommendation with pointwise and pairwise learning

Conference item

Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (\method ) that combines pointwise and pairwise learning for recommendation. \method has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.

Authors: Wang, M; Guo, Y; Zhao, Z; Hu, G; Shen, Y

• Publication status: Published
• Peer review status: Reviewed (other)
• Publisher: Association for Computing Machinery
• Host title: SIGIR 2022: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
• Journal: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval More from this journal
• Pages: 2105-2109
• Publication date: 2022-07-07
• Event title: 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
• Event location: Madrid, Spain
• Event website: https://sigir.org/sigir2022/
• Event start date: 2022-07-11
• Event end date: 2022-07-15
• DOI: 10.1145/3477495.3531813
• ISBN: 9781450387323
• Language: English
• Keywords: FFR