基于加权SimRank的中文查询推荐研究  

Chinese Query Recommendation by Weighted SimRank

作　　者：李亚楠[1,2] 许晟[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》2010年第3期3-10,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金资助项目(60603094);北京市自然科学基金资助项目(4082030);国家973资助项目(2007CB311103);国家863计划资助项目(2006AA010105)

摘　　要：查询推荐是搜索引擎系统中的一项重要技术,其通过推荐更合适的查询以提高用户的搜索体验。现有方法能够找到直接通过某种属性关联的相似查询,却忽略了具有间接关联的语义相关查询。该文将用户查询及查询间直接联系建模为查询关系图,并在图结构相似度算法SimRank的基础上提出了加权SimRank(简称WSimRank)用于查询推荐。WSimRank综合考虑了查询关系图的全局信息,因而能挖掘出查询间的间接关联和语义关系。然而,WSimRank复杂度太高而难以实用,该文将WSimRank转换为一个状态层次图的遍历和计算过程,进而采用动态规划、剪枝等策略对其进行优化从而可以实际应用。在大规模真实Web搜索日志上的实验表明,WSimRank在各项评价指标上均优于SimRank和传统查询推荐方法,其MAP指标接近0.9。Query recommendation as an important technology used in search engines suggests relevant queries to help users to reformulate more accurate queries. Existing approaches of query suggestion compute query similarity based on direct matching of query properties. However, it is hard to find the semantic relevant queries that are related indirectly. In this paper, queries are modeled by a query relation graph where query similarity is computed using WSimRank, a revised algorithm based on SimRank. WSimRank takes the edge information and global structure of query relation graph into account so that it can find the latent semantic relations between queries. To reduce the high complexity of basic WSimRank w. r. t real large query relation graph, this paper changes the WSimRank into a state graph and optimized with dynamic programming and pruning. Experiments on large real search engine query logs show that WSimRank outperforms SimRank and other conventional approaches on query suggestion. The MAP of query suggestions generated by WSimRank achieves nearly 0.9.

关 键 词：计算机应用 中文信息处理 搜索引擎 查询推荐 SIMRANK wSimRank 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]