关键词: 个性化推荐, 集成学习, 弱学习器, 协同过滤

Abstract: After the ending of Netflix Prize contest in 2009，the Ensemble learning method for recommendation including Bagging and Stacking attracts much attention because the top teams wined the prize with this kind of algorithms.However，almost nobody cares the Boosting algorithms for personalized recommendation.This paper first analyzes the reason why Boosting framework can be successfully applied in the classification and points out its drawbacks when used in recommendation.By simplifying and decomposing the user-rating matrix，the original recommendation problem is transformed into a simple classification problem.And then，the Boosting algorithms can be applied into recommendation problems.Thus，a new combined algorithm is proposed，called RankBoost*，which boosts the multiple KNN algorithms using several different similarity measures to improve the final predication performance.Experimental results show the effectiveness of Boosting framework to improve the single learning algorithm for personalized recommendation.

Key words: personalized recommendation, boosting, weak hypothesis, collaborative filtering