Learning to rank from Bayesian decision inference

Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning to rank in document retrieval. Learning-based methods, such as RankNet, RankSVM, and RankBoost, try to create ranking functions automatically by using some training data. Recently, several learning to rank methods have been proposed to directly optimize the performance of IR applications in terms of various evaluation measures. They undoubtedly provide statistically significant improvements over conventional methods; however, from the viewpoint of decision-making, most of them do not minimize the Bayes risk of the IR system. In an attempt to fill this research gap, we propose a novel framework that directly optimizes the Bayes risk related to the ranking accuracy in terms of the IR evaluation measures. The results of experiments on the LETOR collections demonstrate that the framework outperforms several exi...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
• Keywords: CIKM 2009, Database, Document Retrieval, Evaluation Measures, Ranking Function