【作者】 许晟； 李亚楠； 王斌； 吴丽辉；

【Author】 Xu Sheng~1,Li Yanan~1,Wang Bin~1,Wu Lihui~2 1 Institute of Computing Technology,Chinese Academy of Science,Beijing,100190 2 Informationization Division,General Office of Chinese Academy of Sciences,Beijing,100864

【机构】 中国科学院计算技术研究所； 中国科学院办公厅信息化工作处；

【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。本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。为降低WSimRank的计算复杂度,本文其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。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。更多还原

【Abstract】 Query suggestion is an important technology used in search engines.It 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 computational complexity of the basic WSimRank,it is changed 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 some other conventional approaches on query suggestion.The MAP of query suggestions generated by WSimRank achieves nearly 0.9.更多还原