DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910680555192320 |
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| author | Xiao, Shuai Jiang, Zaifan |
| author_facet | Xiao, Shuai Jiang, Zaifan |
| contents | As the last stage of a typical \textit{recommendation system}, \textit{collective recommendation} aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a \textit{\underline{div}ersity-aware self-correcting sequential recommendation \underline{net}works} (\textit{DivNet}) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that \textit{DivNet} can achieve better results compared to baselines with or without collective recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks Xiao, Shuai Jiang, Zaifan Information Retrieval As the last stage of a typical \textit{recommendation system}, \textit{collective recommendation} aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a \textit{\underline{div}ersity-aware self-correcting sequential recommendation \underline{net}works} (\textit{DivNet}) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that \textit{DivNet} can achieve better results compared to baselines with or without collective recommendations. |
| title | DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2411.00395 |