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A Unified Energy-based Framework for Learning to Rank
2016
Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval - ICTIR '16
Learning to Rank (L2R) has emerged as one of the core machine learning techniques for IR. On the other hand, Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. They have produced impressive results in many computer vision and speech recognition tasks. In this paper, we introduce a unified view of Learning to Rank that integrates various L2R approaches in an energy-based ranking framework. In this framework, an
doi:10.1145/2970398.2970416
dblp:conf/ictir/FangL16
fatcat:vd6jtak7xraizemsymy5stk5xq