1998 · 815 citations · 0 references
ComputingComputer ScienceInformation RetrievalMachine LearningRecommender SystemsCollaborative FilteringMulti-criteria RecommendationPersonalized SearchRanking ProblemsRecommendation EvaluationRecommendation SystemsAutomatic Query ExpansionBoostingLearning To RankLearning-to-rank For RecommendationPreference LearningPreference Learning For RankingRanking AlgorithmsSearch Strategies
Combining preferences is relevant for tasks such as aggregating search engine results and ranking movies based on user-provided rankings. The study aims to develop a formal framework for accurately ranking objects by combining multiple ranking or preference functions. The authors propose RankBoost, an efficient boosting-based algorithm for combining preferences, and provide an implementation for a common restricted case. Theoretical analysis shows RankBoost performs well on both training and unseen test data.
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 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.