Learning-to-Rank with Nested Feedback
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917561510133760 |
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| author | Sagtani, Hitesh Jeunen, Olivier Ustimenko, Aleksei |
| author_facet | Sagtani, Hitesh Jeunen, Olivier Ustimenko, Aleksei |
| contents | Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in the Learning-to-Rank (LTR) research literature have enabled rapid growth in this application area. Several popular interfaces now include nested lists, where users can enter a 2nd-level feed via any given 1st-level item. Naturally, this has implications for evaluation metrics, objective functions, and the ranking policies we wish to learn. We propose a theoretically grounded method to incorporate 2nd-level feedback into any 1st-level ranking model. Online experiments on a large-scale recommendation system confirm our theoretical findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04053 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-to-Rank with Nested Feedback Sagtani, Hitesh Jeunen, Olivier Ustimenko, Aleksei Information Retrieval Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in the Learning-to-Rank (LTR) research literature have enabled rapid growth in this application area. Several popular interfaces now include nested lists, where users can enter a 2nd-level feed via any given 1st-level item. Naturally, this has implications for evaluation metrics, objective functions, and the ranking policies we wish to learn. We propose a theoretically grounded method to incorporate 2nd-level feedback into any 1st-level ranking model. Online experiments on a large-scale recommendation system confirm our theoretical findings. |
| title | Learning-to-Rank with Nested Feedback |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2401.04053 |