基于用户特性的搜索引擎查询结果缓存与预取  

Query Results Caching and Prefetching in Web Search Engines Based on User Characteristics

作　　者：马宏远[1,2] 王斌[1] 

机构地区：[1]中国科学院计算技术研究所,北京100190 [2]中国科学院研究生院,北京100049

出　　处：《中文信息学报》2012年第6期19-26,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金资助项目(60873166);国家973资助项目(2007CB311103);国家863计划资助项目(2006AA010105);教育部科学技术研究重点资助项目(109028)

摘　　要：针对搜索引擎查询结果缓存与预取问题,与传统的基于查询特性相关的方法不同,提出了一种基于用户特性的缓存与预取方法,用于提高搜索引擎系统性能,尤其针对部分用户效果更显著。通过对国内某著名商业搜索引擎用户的查询贡献分析得出,用户对搜索引擎的贡献具有长尾分布特性,结合该特性设计查询结果预测模型来进行预取和分区缓存。在该搜索引擎两个月的大规模真实用户查询日志上的实验结果表明,与传统的基于查询特性的典型方法相比,该方法可以获得3.03%～4.17%的命中率提升,对于查询贡献最大的0.25%的用户群体,可以获得20.52%～28.2%的命中率提升。Query results caching and prefetching are crucial to the efficiency of Web search engines. This paper presents a novel approach tailored for query results caching and prefetching based on the user characteristics. We describe an analysis of query logs originated from a famous Web search engine, and design a query results prediction model for prefetching and to partition the cache exploiting the characteristics of the users. We then use a real large scale query logs of 2-months to evaluate the approach, in contrast to the traditional methods and theoretical upper bounds. Experimental results show that this approach can achieve 3.03% to 4.17% increase for all requests as compared with state-of-the-art methods, and 20.52% to 28.2% increase for requests from the special users group who contributes most to Web search engines.

关 键 词：查询结果缓存 用户特性 性能优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]