Multi-intent Aware Contrastive Learning for Sequential Recommendation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913499199832064 |
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| author | Huang, Junshu Long, Zi Fu, Xianghua Chen, Yin |
| author_facet | Huang, Junshu Long, Zi Fu, Xianghua Chen, Yin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-intent Aware Contrastive Learning for Sequential Recommendation Huang, Junshu Long, Zi Fu, Xianghua Chen, Yin Machine Learning 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. |
| title | Multi-intent Aware Contrastive Learning for Sequential Recommendation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.08733 |