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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 capturedoi:10.1145/1935826.1935924 dblp:conf/wsdm/KangWCLCTZ11 fatcat:aqfqeym4b5cghodytkvhotxk4a