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Learning to Rank with Deep Local Context Models

posted Apr 17, 2018, 2:02 PM by Qingyao Ai   [ updated Apr 26, 2018, 4:45 PM ]

Learning to rank has been intensively studied and widely applied in information retrieval. However, the majority of the existing learning-to-rank algorithms model the relativity at the loss level through constructing pairwise or listwise loss functions. They are confined to  pointwise relevance, i.e., the relevance score of a document is computed based on the document itself, regardless of the other documents in the list. In this project, we argue that the relative relevance of a document in a rank list should depend on other top ranked documents, which we refer as the local ranking context. Thus, we propose to use the inherent feature distributions of the top results to capture the listwise context for learning-to-rank systems. [SIGIR'18] [code].