  Extracting demographic features from hidden factors is an innovative concept
that provides multiple and relevant applications. The matrix factorization
model generates factors which do not incorporate semantic knowledge. This paper
provides a deep learning-based method: DeepUnHide, able to extract demographic
information from the users and items factors in collaborative filtering
recommender systems. The core of the proposed method is the gradient-based
localization used in the image processing literature to highlight the
representative areas of each classification class. Validation experiments make
use of two public datasets and current baselines. Results show the superiority
of DeepUnHide to make feature selection and demographic classification,
compared to the state of art of feature selection methods. Relevant and direct
applications include recommendations explanation, fairness in collaborative
filtering and recommendation to groups of users.
