在疯狂的网络中识别查询

Jingjing Liu, Chang Liu, Jun Zhang, R. Bierig, Michael J. Cole
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引用次数: 0

摘要

识别用户查询行为是信息查找和检索研究中的一个重要问题。与查询相关的研究通常依赖于从单个搜索引擎获取的服务器端日志,但是要全面了解用户查询行为,需要分析从客户端收集的数据,以便进行不受限制的搜索。我们开发了三种方法来识别查询行为,并在实验室实验中收集的客户端日志上对它们进行了测试,这些日志用于实际任务和整个Web上的无限制搜索。结果表明,最好的方法能够识别97%的查询,准确率为92%。尽管基于相对较少的搜索集,但我们的方法(可能只进行了很少的修改)应该足以识别无约束Web搜索日志中的查询。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying queries in the wild, wild web
Identifying user querying behavior is an important problem for information seeking and retrieval research. Query-related studies typically rely on server-side logs taken from a single search engine, but a comprehensive view of user querying behaviors requires analysis of data collected from the client-side for unrestricted searches. We developed three methods to identify querying behaviors and tested them on client-side logs collected in a lab experiment for realistic tasks and unrestricted searches on the entire Web. Results show that the best method was able to identify 97% of queries issued, with a precision of 92%. Although based on a relatively small number of search episodes, our methods, perhaps with minimal modifications, should be adequate for identification of queries in logs of unconstrained Web search.
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