Multi-intent Aware Contrastive Learning for Sequential Recommendation

Junshu Huang; Zi Long; Xianghua Fu; Yin Chen

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arXiv · 2409.08733

Multi-intent Aware Contrastive Learning for Sequential Recommendation

Junshu Huang·Zi Long·Xianghua Fu·Yin Chen

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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.

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Junshu Huang, Zi Long, Xianghua Fu, Yin Chen. 2024-09-13. Multi-intent Aware Contrastive Learning for Sequential Recommendation. https://arxiv.org/abs/2409.08733

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