2022/02/05 by Yongjun Chen, Zhiwei Liu, Jia Li +2 · 390 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Graph Neural Networks #Artificial intelligence #Cluster analysis #Computer science #Feature learning #Latent variable #Latent variable model #Leverage (statistics) #Machine learning #Recommender Systems and Techniques #Robustness (evolution) #cs.AI
paper · pdf · doi:10.1145/3485447.3512090
published in Proceedings of the ACM Web Conference 2022, 2172-2182
arxiv created 2022/02/05 · arxiv updated 2022/02/08 · openalex publication_date 2022/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Users’ interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.). However, users’ underlying intents are often unobserved/latent, making it challenging to leverage such latent intents for Sequential recommendation (SR). To investigate the benefits of latent intents and leverage them effectively for recommendation, we propose Intent Contrastive Learning (ICL), a general learning paradigm that leverages a latent intent variable into SR. The core idea is to learn users’ intent distribution functions from unlabeled user behavior sequences and optimize SR models with contrastive self-supervised learning (SSL) by considering the learnt intents to improve recommendation. Specifically, we introduce a latent variable to represent users’ intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learnt intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. The training is alternated between intent representation learning and the SR model optimization steps within the generalized expectation-maximization (EM) framework. Fusing user intent information into SR also improves model robustness. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm, which improves performance, and robustness against data sparsity and noisy interaction issues 1.