Context-Aware Recommender System Based on Boolean Matrix Factorisation

In this work we propose and study an approach for collabora- tive filtering, which is based on Boolean matrix factorisation and exploits additional (context) information about users and items. To avoid simi- larity loss in case of Boolean representation we use an adjusted type of projection of a target user to the obtained factor space. We have com- pared the proposed method with SVD-based approach on the MovieLens dataset. The experiments demonstrate that the proposed method has better MAE and Precision and comparable Recall and F-measure. We also report an increase of quality in the context information presence.

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