Multi-intent Aware Contrastive Learning for Sequential Recommendation

Junshu Huang; Zi Long; Xianghua Fu; Yin Chen

Multi-intent Aware Contrastive Learning for Sequential Recommendation

Machine Learning 2024-09-16 v1

Authors: Junshu Huang , Zi Long , Xianghua Fu , Yin Chen

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.

Keywords

intent detection recommendation system contrastive learning

Cite

@article{arxiv.2409.08733,
  title  = {Multi-intent Aware Contrastive Learning for Sequential Recommendation},
  author = {Junshu Huang and Zi Long and Xianghua Fu and Yin Chen},
  journal= {arXiv preprint arXiv:2409.08733},
  year   = {2024}
}

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