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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2401.13351 |
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| _version_ | 1866916103995785216 |
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| author | Vicente-López, Eduardo de Campos, Luis M. Fernández-Luna, Juan M. Huete, Juan F. |
| author_facet | Vicente-López, Eduardo de Campos, Luis M. Fernández-Luna, Juan M. Huete, Juan F. |
| contents | Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13351 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting IR Personalization Performance using Pre-retrieval Query Predictors Vicente-López, Eduardo de Campos, Luis M. Fernández-Luna, Juan M. Huete, Juan F. Information Retrieval Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements. |
| title | Predicting IR Personalization Performance using Pre-retrieval Query Predictors |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2401.13351 |