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IntervalRank - Isotonic Regression with Listwise and Pairwise Constraints

11 years 4 months ago
IntervalRank - Isotonic Regression with Listwise and Pairwise Constraints
Ranking a set of retrieved documents according to their relevance to a given query has become a popular problem at the intersection of web search, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focuses on a different aspect of the dataset while largely ignoring others. The current paper shows how a combination of them can lead to improved ranking performance and, moreover, how it can be implemented in log-linear time. The basic idea of the algorithm is to use isotonic regression with adaptive bandwidth selection per relevance grade. This results in an implicitly-defined loss function which can be minimized efficiently by a subgradient descent procedure. Experimental results show that the resulting algorithm is competitive on both commercial search engine data and publicly available LETOR data sets. Categories and Subject Descriptors H.3.3 [...
Taesup Moon, Alex Smola, Yi Chang, Zhaohui Zheng
Added 17 Jul 2010
Updated 17 Jul 2010
Type Conference
Year 2010
Where WSDM
Authors Taesup Moon, Alex Smola, Yi Chang, Zhaohui Zheng
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