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.
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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. https://doi.org/10.1007/978-3-319-23525-7_1
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DOI: https://doi.org/10.1007/978-3-319-23525-7_1
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