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Counterfactual Online Learning to Rank
Exploiting users’ implicit feedback, such as clicks, to learn rankers is attractive as it does not require editorial labelling effort, and adapts to users’ changing preferences, among other benefits. However, directly learning a ranker from implicit data is challenging, as users’ implicit feedback u...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148247/ http://dx.doi.org/10.1007/978-3-030-45439-5_28 |