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Indexed by:会议论文

Date of Publication:2016-11-30

Included Journals:EI、CPCI-S

Volume:9994

Page Number:329-334

Key Words:Information retrieval; Learning to rank; Group ranking

Abstract:According to a given query in training set, the documents can be grouped based on their relevance judgments. If the group with higher relevance labels is in front of the one with lower relevance judgments, the ranking performance of ranking model could be perfect. Inspired by this idea, we propose a novel machine learning framework for ranking, which depends on two new samples. The first sample is one-group constructed of one document with higher relevance judgment and a group of documents with lower relevance judgment; the second sample is group-group constructed of a group of documents with higher relevance judgment and a group of documents with lower relevance judgment. We also develop a novel preference-weighted loss function for multiple relevance judgment data sets. Finally, we optimize the group ranking approaches by optimizing initial ranking list for likelihood loss function. Experimental results show that our approaches are effective in improving ranking performance.