基于流形排序的查询推荐方法

针对传统查询推荐方法中存在的相关性度量问题和冗余性问题,该文中提出了一种新的基于流形排序的查询推荐方法。该方法利用查询数据内在的全局流形结构来获得查询之间的相关性,可以有效避免传统方法中相关性度量对高维稀疏查询数据处理的不足;同时,该方法通过提升结构上具有代表性的查询来达到减小查询推荐的冗余性。在一个大规模商业搜索引擎查询日志上的实验结果表明:使用流形排序的查询推荐方法要优于传统查询推荐方法和现有的Hitting-time Ranking方法。

Query Recommendation Based on Manifold Ranking

To address problems of both relevance measurement and redundance in traditional query recommendation approaches, in this paper, we propose a novel query recommendation approach based on Manifold Ranking. This approach exploites the intrinsic global manifold structure to capture the relevance among queries, and effectively avoids the deficiency of the relevance measurement in traditional approaches when dealing with high-dimensional query data. Meanwhile, it also reduces the redundance by boosting representative queries in the structure. Empirical experiments on a large scale query log of a commercial search engine show that query recommendation using Manifold Ranking is superior to both the traditional approach and the existing Hitting-time Ranking approach.
Key wordsquery recommendation; manifold ranking; click-through data