Deep Learning feature selection to unhide demographic recommender systems factors
This addresses the need for explainable and fair recommendations in collaborative filtering systems, though it is incremental as it adapts gradient-based localization from image processing.
The paper tackled the problem of extracting demographic information from hidden factors in collaborative filtering recommender systems, resulting in DeepUnHide, a deep learning-based method that outperformed state-of-the-art feature selection methods in demographic classification.
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.