Zeroshot Listwise Learning to Rank Algorithm for Recommendation
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912039292633088 |
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| author | Wang, Hao |
| author_facet | Wang, Hao |
| contents | Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13703 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zeroshot Listwise Learning to Rank Algorithm for Recommendation Wang, Hao Information Retrieval Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair. |
| title | Zeroshot Listwise Learning to Rank Algorithm for Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2409.13703 |