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Multi-intent Aware Contrastive Learning for Sequential Recommendation

arXiv 2409.08733 ↗First posted 13 Sep 2024cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 13 Sep 2024.

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32 of 49 references.

References · 32

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20 more
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