基于Term-Query-URL异构信息网络的查询推荐

查询推荐是一种帮助搜索引擎更好的理解用户检索需求的方法.基于查询的上下文片段训练词汇和查询之间的语义关系,同时结合查询和URL的点击图以及查询中的序列行为构建Term-Query-URL异构信息网络,采用重启动随机游走(Random Walk with Restart,RWR)进行查询推荐.综合利用语义信息和日志信息,提高了稀疏查询的推荐效果.基于概率语言模型构造查询的词汇向量,可以为新的查询进行查询推荐.在大规模商业搜索引擎查询日志上的实验表明本文方法相比传统的查询推荐方法性能提升约为3%~10%.

Query Suggestion by Constructing Heterogeneous Term-Query-URL Information Network

Query suggestion is an interactive approach for search engines to better understand user information need. A Term-Query bipartite graph was trained by extracting semantic relationships from snippet clicked by query. With the combination of Query-URL graph and Query-Flow graph, a heterogeneous Term-Query-URL information network was constructed. Random walk with restart (RWR) was performed on the information network for query suggestion. The relevance of long tail query suggestion was greatly improved by taking into account semantic information and log information. Term vector of query was constructed on the basis of probabilistic language model for query suggestion of new query. The experiment results have shown that our approach outperforms baseline methods by about 3% to 10%.