BoostMF: Boosted Matrix Factorisation for Collaborative Ranking
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.
Cite
Text
Chowdhury et al. "BoostMF: Boosted Matrix Factorisation for Collaborative Ranking." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23525-7_1Markdown
[Chowdhury et al. "BoostMF: Boosted Matrix Factorisation for Collaborative Ranking." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/chowdhury2015ecmlpkdd-boostmf/) doi:10.1007/978-3-319-23525-7_1BibTeX
@inproceedings{chowdhury2015ecmlpkdd-boostmf,
title = {{BoostMF: Boosted Matrix Factorisation for Collaborative Ranking}},
author = {Chowdhury, Nipa and Cai, Xiongcai and Luo, Cheng},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {3-18},
doi = {10.1007/978-3-319-23525-7_1},
url = {https://mlanthology.org/ecmlpkdd/2015/chowdhury2015ecmlpkdd-boostmf/}
}