自适应个性化会话信息检索
Adaptive Personalized Conversational Information Retrieval
AI总结：
本文提出自适应个性化会话信息检索框架APCIR，通过动态识别查询的个性化级别并结合感知个性化的排序融合方法，在TREC iKAT数据集上有效提升了检索性能。
摘要：
个性化会话信息检索（CIR）系统旨在通过考虑用户画像，在多轮交互中满足用户复杂的信息需求。然而，并非所有搜索查询都需要个性化。挑战在于如何在需要时适当地将个性化元素融入搜索中。大多数现有研究使用大语言模型隐式地结合用户个人信息和会话上下文，而没有区分每个查询轮次的具体要求。这种“一刀切”的个性化策略可能导致次优结果。在本文中，我们提出了一种自适应个性化方法，首先识别查询所需的个性化级别，并将个性化查询与其他查询重构相结合，以生成各种增强查询。然后，我们设计了一种感知个性化的排序融合方法，根据所需的个性化级别动态地为不同的重构查询分配融合权重。所提出的自适应个性化会话信息检索框架 APCIR 在两个 TREC iKAT 数据集上进行了评估。结果证实了 APCIR 自适应个性化的有效性，其表现优于现有最先进的方法。
英文摘要：
Personalized conversational information retrieval (CIR) systems aim to satisfy users' complex information needs through multi-turn interactions by considering user profiles. However, not all search queries require personalization. The challenge lies in appropriately incorporating personalization elements into search when needed. Most existing studies implicitly incorporate users' personal information and conversational context using large language models without distinguishing the specific requirements for each query turn. Such a ``one-size-fits-all'' personalization strategy might lead to sub-optimal results. In this paper, we propose an adaptive personalization method, in which we first identify the required personalization level for a query and integrate personalized queries with other query reformulations to produce various enhanced queries. Then, we design a personalization-aware ranking fusion approach to assign fusion weights dynamically to different reformulated queries, depending on the required personalization level. The proposed adaptive personalized conversational information retrieval framework APCIR is evaluated on two TREC iKAT datasets. The results confirm the effectiveness of adaptive personalization of APCIR by outperforming state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
