Towards Principled Learning for Re-ranking in Recommender Systems
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912311323656192 |
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| author | Li, Qunwei Li, Linghui Lin, Jianbin Zhong, Wenliang |
| author_facet | Li, Qunwei Li, Linghui Lin, Jianbin Zhong, Wenliang |
| contents | As the final stage of recommender systems, re-ranking presents ordered item lists to users that best match their interests. It plays such a critical role and has become a trending research topic with much attention from both academia and industry. Recent advances of re-ranking are focused on attentive listwise modeling of interactions and mutual influences among items to be re-ranked. However, principles to guide the learning process of a re-ranker, and to measure the quality of the output of the re-ranker, have been always missing. In this paper, we study such principles to learn a good re-ranker. Two principles are proposed, including convergence consistency and adversarial consistency. These two principles can be applied in the learning of a generic re-ranker and improve its performance. We validate such a finding by various baseline methods over different datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Principled Learning for Re-ranking in Recommender Systems Li, Qunwei Li, Linghui Lin, Jianbin Zhong, Wenliang Information Retrieval Machine Learning As the final stage of recommender systems, re-ranking presents ordered item lists to users that best match their interests. It plays such a critical role and has become a trending research topic with much attention from both academia and industry. Recent advances of re-ranking are focused on attentive listwise modeling of interactions and mutual influences among items to be re-ranked. However, principles to guide the learning process of a re-ranker, and to measure the quality of the output of the re-ranker, have been always missing. In this paper, we study such principles to learn a good re-ranker. Two principles are proposed, including convergence consistency and adversarial consistency. These two principles can be applied in the learning of a generic re-ranker and improve its performance. We validate such a finding by various baseline methods over different datasets. |
| title | Towards Principled Learning for Re-ranking in Recommender Systems |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2504.04188 |