Multi-intent Aware Contrastive Learning for Sequential Recommendation
This addresses the need for more accurate recommendation systems by incorporating multiple intents, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of oversimplifying user intents in sequential recommendation by proposing a multi-intent aware contrastive learning approach, which aims to better reflect real-world scenarios compared to single-intent models.
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.