Simulation of within-session query variations using a text segmentation approach

We propose a generative model for automatic query refor- mulations from an initial query using the underlying subtopic structure of top ranked retrieved documents. We address three types of query reformulations a) specialization; b) generalization; and c) drift. To test our model we generate the three reformulation variants starting with selected fields from the TREC-8 topics as the initial queries. We use manual judgments from multiple assessors to calculate the accuracy of the reformulated query variants and observe accuracies of 65%, 82% and 69% respectively for specialization, generalization and drift reformulations

查询推荐研究综述

查询推荐是一种提高用户搜索效率的重要技术，其核心任务是帮助用户构造有效查询并以此准确描述用户信息需求。作为当今搜索引擎的核心技术之一，查询推荐吸引了学术界和工业界的广泛关注，一直以来都是信息检索领域中重要的研究主题。本文以国内外会议、期刊发表的有关查询推荐研究的文献为对象，利用归纳总结方法，首先详细梳理了查询推荐中主流方法&mdash;&mdash;基于简单共现信息的方法、基于图模型的方法以及融合多种信息的方法，然后总结评述了评测方法与指标，最后分析了未来可能的研究方向。</p