Boostmf: Boosted Matrix Factorisation For Collaborative Ranking
ECMLPKDD'15: Proceedings of the 2015th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II(2015)
摘要
Personalised recommender systems are widely used information filtering for information retrieval, where matrix factorisation (MF) has become popular as a model-based approach to personalised recommendation. Classical MF methods, which directly approximate low rank factor matrices by minimising some rating prediction criteria, do not achieve a satisfiable performance for the task of top-N recommendation. In this paper, we propose a novel MF method, namely BoostMF, that formulates factorisation as a learning problem and integrates boosting into factorisation. Rather than using boosting as a wrapper, BoostMF directly learns latent factors that are optimised toward the top-N recommendation. The proposed method is evaluated against a set of stateof- the-art methods on three popular public benchmark datasets. The experimental results demonstrate that the proposed method achieves significant improvement over these baseline methods for the task of top-N recommendation.
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关键词
Recommender system,Collaborative filtering,Matrix factorisation,Learning to rank,Boosting
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