An Efficient Boosting Algorithm for Combining Preferences

We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several applications, such as that of combining the results of different search engines, or the "collaborative-filtering" problem of ranking movies for a user based on the movie rankings provided by other users. In this work, we begin by presenting a formal framework for this general problem. We then describe and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning. We give theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training. We also describe an efficient implementation of the algorithm for a particular restricted but common case. We next discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, w...

• Type: Conference
• Year: 1998
• Where: ICML
• Authors: Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer
• Keywords: Algorithm Called Rankboost, General Problem, ICML 1998, Machine Learning, Preferences Arises