按挣扎流图挣扎搜索查询建议

Zebang Chen, Takehiro Yamamoto, Katsumi Tanaka
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引用次数: 6

摘要

我们提出了一种方法来生成有效的查询建议,旨在帮助用户在搜索会话中定位与他们的信息需求相关的信息时遇到困难的搜索。核心是识别正在进行的挣扎会话的挣扎部分,并挖掘其有效表现形式。挣扎组件是用户在挣扎会话期间努力寻找有效表示的信息需求的语义组件。该方法识别给定正在进行的挣扎会话的挣扎组件,并从查询日志中挖掘包含所识别的挣扎组件的会话,以构建挣扎流图。挣扎流图记录了用户对挣扎组件项的重新表述行为,通过挣扎流图可以挖掘出挣扎组件的有效表示。实验结果表明,当该方法可以在一个冲突会话中使用两个或多个查询时,其性能优于基线方法。
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Query Suggestion for Struggling Search by Struggling Flow Graph
We propose a method to generate effective query suggestions aiming to help struggling search, where users experience difficulty in locating information that is relevant to their information need in the search session. The core is identifying struggling component of an on-going struggling session and mining the effective representations of it. The struggling component is the semantic component of information need for which the user struggled to find an effective representation during the struggling session. The proposed method identifies the struggling component of given on-going struggling session and mines the sessions containing the identified struggling component from a query log to build a struggling flow graph. The struggling flow graph records users' reformulation behaviors for the terms of the struggling component, through struggling flow graph we can mine effective representations of the struggling component. The experimental results demonstrate that the proposed method outperforms the baseline methods when it can use two or more queries in a struggling session.
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