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.%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.
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