Weak Supervision for Improved Precision in Search Systems
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | |
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| _version_ | 1866910866930139136 |
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| author | Vasudevan, Sriram |
| author_facet | Vasudevan, Sriram |
| contents | Labeled datasets are essential for modern search engines, which increasingly rely on supervised learning methods like Learning to Rank and massive amounts of data to power deep learning models. However, creating these datasets is both time-consuming and costly, leading to the common use of user click and activity logs as proxies for relevance. In this paper, we present a weak supervision approach to infer the quality of query-document pairs and apply it within a Learning to Rank framework to enhance the precision of a large-scale search system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weak Supervision for Improved Precision in Search Systems Vasudevan, Sriram Information Retrieval Artificial Intelligence Machine Learning Labeled datasets are essential for modern search engines, which increasingly rely on supervised learning methods like Learning to Rank and massive amounts of data to power deep learning models. However, creating these datasets is both time-consuming and costly, leading to the common use of user click and activity logs as proxies for relevance. In this paper, we present a weak supervision approach to infer the quality of query-document pairs and apply it within a Learning to Rank framework to enhance the precision of a large-scale search system. |
| title | Weak Supervision for Improved Precision in Search Systems |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.07025 |