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.

翻译：应用计算语言学研究（RALI）团队参与了2024年TREC交互式知识辅助（iKAT）赛道。在个性化对话式搜索中，为了有效捕捉用户复杂的搜索意图，需要在查询重构中同时融入上下文信息及用户档案中的关键要素。用户档案通常包含许多相关片段，其中每一个都可能补充用户的信息需求。很难完全忽略其中任何一个，而引入过多此类片段又存在偏离原始查询的风险，并会损害搜索性能。这一挑战我们称之为过度个性化。为解决此问题，我们提出了不同的策略，通过融合基于不同个性化程度的查询所生成的排序列表来实现。