2003 AnEfficientBoostingAlgForCombPref

Subject Headings: RankBoost Algorithm, Rank Function Learning Task, Preference Function.

Notes

(Xu & Li, 2007) ⇒ Jun Xu, and Hang Li. (2007). “AdaRank: A Boosting Algorithm for Information Retrieval.” In: Proceedings of the 30th annual international ACM SIGIR conference doi:10.1145/1277741.1277809

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, we used the algorithm to combine different web search strategies, each of which is a query expansion for a given domain. The second experiment is a collaborative-filtering task for making movie recommendations.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 AnEfficientBoostingAlgForCombPref	Yoav Freund Raj Iyer Robert E. Schapire Yoram Singer			An Efficient Boosting Algorithm for Combining Preferences		The Journal of Machine Learning Research	http://www.jmlr.org/papers/volume4/freund03a/freund03a.pdf			2003