An FCA-based Boolean Matrix Factorisation for Collaborative Filtering
This addresses collaborative filtering for recommendation systems, but it appears incremental as it adapts existing methods to binary data.
The authors tackled collaborative filtering by proposing a Boolean Matrix Factorisation approach using Formal Concept Analysis, achieving comparable Mean Average Error to SVD-based algorithms on the Movielens dataset while using binary-scaled rating data instead of non-scaled data.
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.