RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in Conversational Search
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909424466001920 |
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| author | Hui, Yuchen Mo, Fengran Mao, Milan Nie, Jian-Yun |
| author_facet | Hui, Yuchen Mo, Fengran Mao, Milan Nie, Jian-Yun |
| contents | The Recherche Appliquee en Linguistique Informatique (RALI) team participated in the 2024 TREC Interactive Knowledge Assistance (iKAT) Track. In personalized conversational search, effectively capturing a user's complex search intent requires incorporating both contextual information and key elements from the user profile into query reformulation. The user profile often contains many relevant pieces, and each could potentially complement the user's information needs. It is difficult to disregard any of them, whereas introducing an excessive number of these pieces risks drifting from the original query and hinders search performance. This is a challenge we denote as over-personalization. To address this, we propose different strategies by fusing ranking lists generated from the queries with different levels of personalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in Conversational Search Hui, Yuchen Mo, Fengran Mao, Milan Nie, Jian-Yun Information Retrieval The Recherche Appliquee en Linguistique Informatique (RALI) team participated in the 2024 TREC Interactive Knowledge Assistance (iKAT) Track. In personalized conversational search, effectively capturing a user's complex search intent requires incorporating both contextual information and key elements from the user profile into query reformulation. The user profile often contains many relevant pieces, and each could potentially complement the user's information needs. It is difficult to disregard any of them, whereas introducing an excessive number of these pieces risks drifting from the original query and hinders search performance. This is a challenge we denote as over-personalization. To address this, we propose different strategies by fusing ranking lists generated from the queries with different levels of personalization. |
| title | RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in Conversational Search |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.07998 |