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Extracting demographic features from hidden factors is an innovative conceptthat provides multiple and relevant applications. The matrix factorizationmodel generates factors which do not incorporate semantic knowledge. This paperprovides a deep learning-based method: DeepUnHide, able to extract demographicinformation from the users and items factors in collaborative filteringrecommender systems. The core of the proposed method is the gradient-basedlocalization used in the image processing literature to highlight therepresentative areas of each classification class. Validation experiments makeuse of two public datasets and current baselines. Results show the superiorityof DeepUnHide to make feature selection and demographic classification,compared to the state of art of feature selection methods. Relevant and directapplications include recommendations explanation, fairness in collaborativefiltering and recommendation to groups of users.

Deep Learning feature selection to unhide demographic recommender systems factors