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2008

Global Ranking Using Continuous Conditional Random Fields

11 years 1 months ago
Global Ranking Using Continuous Conditional Random Fields
This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for `local ranking', in the sense that the ranking model is defined on a single object, for example, a document in information retrieval. For many applications, this is a very loose approximation. Relations always exist between objects and it is better to define the ranking model as a function on all the objects to be ranked (i.e., the relations are also included). This paper refers to the problem as global ranking and proposes employing a Continuous Conditional Random Fields (CRF) for conducting the learning task. The Continuous CRF model is defined as a conditional probability distribution over ranking scores of objects conditioned on the objects. It can naturally represent the content information of objects as well as the relation information between objects, necessary for global ranking. Taking two specific information retrieval tasks as examples, ...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Hang Li
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