Deep Learning feature selection to unhide demographic recommender systems factors

Abstract

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.

arXiv comment20 pages, 14 figures, 1 table

References in corpus (7)

• Improving fairness in machine learning systems: What do industry practitioners need?
• A systematic review and taxonomy of explanations in decision support and recommender systems
• Beyond Parity: Fairness Objectives for Collaborative Filtering
• User Fairness in Recommender Systems
• Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
• Bias Disparity in Recommendation Systems
• DeepFair: Deep Learning for Improving Fairness in Recommender Systems

Cited by in corpus (1)

• Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets