We propose a new approach for Collaborative Filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (Movielens dataset) we compare the approach with the SVD- and NMF-based algorithms in terms of Mean Average Error (MAE). One of the experimental consequences is that it is enough to have a binary-scaled rating data to obtain almost the same quality in terms of MAE by BMF than for the SVD-based algorithm in case of non-scaled data.
@article{arxiv.1310.4366,
title = {An FCA-based Boolean Matrix Factorisation for Collaborative Filtering},
author = {Elena Nenova and Dmitry I. Ignatov and Andrey V. Konstantinov},
journal= {arXiv preprint arXiv:1310.4366},
year = {2013}
}