%0 Journal Article %1 Matuszyk2017 %A Matuszyk, Pawel %A Spiliopoulou, Myra %D 2017 %J Machine Learning %K from:matuszyk myown %P 1--28 %R 10.1007/s10994-016-5614-4 %T Stream-based semi-supervised learning for recommender systems %U https://kmd.cs.ovgu.de/pub/matuszyk/Stream-based-Semi-supervised-Learning-for-RS.pdf %X To alleviate the problem of data sparsity inherent to recommender systems, we propose a semi-supervised framework for stream-based recommendations. Our framework uses abundant unlabelled information to improve the quality of recommendations. We extend a state-of-the-art matrix factorization algorithm by the ability to add new dimensions to the matrix at runtime and implement two approaches to semi-supervised learning: co-training and self-learning. We introduce a new evaluation protocol including statistical testing and parameter optimization. We then evaluate our framework on five real-world datasets in a stream setting. On all of the datasets our method achieves statistically significant improvements in the quality of recommendations.

@article{Matuszyk2017, abstract = {To alleviate the problem of data sparsity inherent to recommender systems, we propose a semi-supervised framework for stream-based recommendations. Our framework uses abundant unlabelled information to improve the quality of recommendations. We extend a state-of-the-art matrix factorization algorithm by the ability to add new dimensions to the matrix at runtime and implement two approaches to semi-supervised learning: co-training and self-learning. We introduce a new evaluation protocol including statistical testing and parameter optimization. We then evaluate our framework on five real-world datasets in a stream setting. On all of the datasets our method achieves statistically significant improvements in the quality of recommendations.}, added-at = {2017-02-08T11:53:04.000+0100}, author = {Matuszyk, Pawel and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/2c077e9a7b3f48c3767aa48fb72e5d3db/kmd-ovgu}, doi = {10.1007/s10994-016-5614-4}, interhash = {73baf1e02f8dcebf1a4eca232c58b6bb}, intrahash = {c077e9a7b3f48c3767aa48fb72e5d3db}, issn = {1573-0565}, journal = {Machine Learning}, keywords = {from:matuszyk myown}, pages = {1--28}, timestamp = {2018-03-09T12:46:02.000+0100}, title = {Stream-based semi-supervised learning for recommender systems}, url = {https://kmd.cs.ovgu.de/pub/matuszyk/Stream-based-Semi-supervised-Learning-for-RS.pdf}, year = 2017 }