Learning to re-rank web search results with multiple pairwise features

Changsung Kang, Xuanhui Wang, Jiang Chen, Ciya Liao, Yi Chang, Belle Tseng, Zhaohui Zheng
2011 Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11  
Web search ranking functions are typically learned to rank search results based on features of individual documents, i.e., pointwise features. Hence, the rich relationships among documents, which contain multiple types of useful information, are either totally ignored or just explored very limitedly. In this paper, we propose to explore multiple pairwise relationships between documents in a learning setting to rerank search results. In particular, we use a set of pairwise features to capture
more » ... ious kinds of pairwise relationships and design two machine learned re-ranking methods to effectively combine these features with a base ranking function: a pairwise comparison method and a pairwise function decomposition method. Furthermore, we propose several schemes to estimate the potential gains of our re-ranking methods on each query and selectively apply them to queries with high confidence. Our experiments on a large scale commercial search engine editorial data set show that considering multiple pairwise relationships is quite beneficial and our proposed methods can achieve significant gain over methods which only consider pointwise features or a single type of pairwise relationship.
doi:10.1145/1935826.1935924 dblp:conf/wsdm/KangWCLCTZ11 fatcat:aqfqeym4b5cghodytkvhotxk4a