Learning Positional Attention for Sequential Recommendation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916495582298112 |
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| author | Luo, Fan He, Haibo Zhang, Juan Xu, Shenghui |
| author_facet | Luo, Fan He, Haibo Zhang, Juan Xu, Shenghui |
| contents | Self-attention-based networks have achieved remarkable performance in sequential recommendation tasks. A crucial component of these models is positional encoding. In this study, we delve into the learned positional embedding, demonstrating that it often captures the distance between tokens. Building on this insight, we introduce novel attention models that directly learn positional relations. Extensive experiments reveal that our proposed models, \textbf{PARec} and \textbf{FPARec} outperform previous self-attention-based approaches. The code can be found here: https://github.com/NetEase-Media/FPARec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Positional Attention for Sequential Recommendation Luo, Fan He, Haibo Zhang, Juan Xu, Shenghui Information Retrieval Computer Vision and Pattern Recognition Self-attention-based networks have achieved remarkable performance in sequential recommendation tasks. A crucial component of these models is positional encoding. In this study, we delve into the learned positional embedding, demonstrating that it often captures the distance between tokens. Building on this insight, we introduce novel attention models that directly learn positional relations. Extensive experiments reveal that our proposed models, \textbf{PARec} and \textbf{FPARec} outperform previous self-attention-based approaches. The code can be found here: https://github.com/NetEase-Media/FPARec. |
| title | Learning Positional Attention for Sequential Recommendation |
| topic | Information Retrieval Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.02793 |