Multi-intent Aware Contrastive Learning for Sequential Recommendation

TL;DR

This paper introduces a multi-intent aware contrastive learning approach for sequential recommendation, aiming to better capture diverse user intents and improve recommendation accuracy.

Contribution

It proposes a novel multi-intent contrastive learning framework that more accurately models real-world user behavior in sequential recommendation systems.

Findings

Enhanced recommendation performance with multi-intent modeling

Better representation of user behavior diversity

Improved alignment with real-world scenarios

Abstract

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.