Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 3-18

BoostMF: Boosted Matrix Factorisation for Collaborative Ranking

Conference paper

DOI: 10.1007/978-3-319-23525-7_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)
Cite this paper as:
Chowdhury N., Cai X., Luo C. (2015) BoostMF: Boosted Matrix Factorisation for Collaborative Ranking. In: Appice A., Rodrigues P., Santos Costa V., Gama J., Jorge A., Soares C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9285. Springer, Cham

Abstract

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 state-of-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.

Keywords

Recommender system Collaborative filtering Matrix factorisation Learning to rank Boosting 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The University of New South WalesSydneyAustralia

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