Active Learning for Ranking through Expected Loss Optimization
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Active Learning for Ranking through Expected Loss Optimization
Abstract— Active Learning for Ranking through Expected Loss Optimization. Learning to rank arises in many data mining applications, ranging from web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking model is strongly affected by the number of labeled examples in the < Final Year Projects 2016 > training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning for ranking. In this paper, we propose a general active learning framework, expected loss optimization (ELO), for ranking. The ELO framework is applicable to a wide range of ranking functions.
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