Paper ID: 2409.08733

Multi-intent Aware Contrastive Learning for Sequential Recommendation

Junshu Huang, Zi Long, Xianghua Fu, Yin Chen

Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on single-intent representations to direct the training process. However, this paradigm oversimplifies real-world recommendation scenarios, attempting to encapsulate the diversity of intents within the single-intent level representation. SR models considering multi-intent information in their framework are more likely to reflect real-life recommendation scenarios accurately.

Submitted: Sep 13, 2024