基于权重标准化SimRank方法的查询扩展技术研究

查询扩展是信息检索中的一项重要技术。传统的局部分析查询扩展方法利用伪相关文档作为候选词集合,然而部分伪相关文档并不具有很高的相关性。该文利用真实的搜索引擎查询日志,建立了查询点击图,经过多次图结构的转化得到能够反映词之间关联程度的词项关系图,并在图结构的相似度算法SimRank的基础上,提出了一种基于权重标准化的改进SimRank方法,该方法利用词项关系图中词项的全局和间接关系,能够有效挖掘与原始查询相关联的扩展词。同时,为降低SimRank算法的计算复杂度,该文采用了剪枝等策略进行优化,使得计算效率有大幅提高。在TREC标准数据集上的实验表明,该文的方法可以有效地选择相关扩展词。MAP指标较局部分析查询扩展方法提高了1.81%,在P@10和P@20指标评价中效果分别提高了5.44%和3.73%。

A Weight Normalization Based SimRank Approach for Query Expansion

As an important technology in information retrieval, and traditional query expansion uses the pseudo-relevant documents as the candidate words set. But some of pseudo-relevant documents are not highly relevant. In our work, a query-click graph is built by a query log in real search engine. The term relationship graph which was obtained by several transformations reflects the direct relationship of the terms. We propose a weight normalization based SimRank approach—a revised algorithm based on the SimRank for the query expansion. In order to reduce the computational complexity of SimRank, strategies like pruning are used to optimize the algorithm. Experiments on large real AOL search engine query logs and a standard TREC corpus shows that our approach can discover the quality expansion terms effectively. The MAP of our approach is 1.81% higher than the query expansion based on pseudo relevance feedback, 5.44% higher on P@10, and 3.73% higher on P@20.
Key wordssearch engine; query expansion; query logs; SimRank