Robust Matrix Completion for Discrete Rating-Scale Data: Coping with Fake Profiles in Recommender Systems

• URL: http://arxiv.org/abs/2412.20802v2
• Date: Tue, 29 Jul 2025 11:54:33 GMT
• Title: Robust Matrix Completion for Discrete Rating-Scale Data: Coping with Fake Profiles in Recommender Systems
• Authors: Aurore Archimbaud, Andreas Alfons, Ines Wilms,
• Abstract summary: Matrix completion is used to predict a user's preferences for items they have not yet rated.<n>We propose a robust discrete matrix completion (RDMC) method to handle the discrete nature of sparse rating-scale data.<n>Our work offers a statistically-sound blueprint for future studies on how to evaluate matrix completion methods for recommender systems under realistic scenarios.
• Score: 1.8638865257327277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems are essential tools in the digital landscape for connecting users with content that more closely aligns with their preferences. Matrix completion is a widely used statistical framework for such systems, aiming to predict a user's preferences for items they have not yet rated by leveraging the observed ratings in a partially filled user-item rating matrix. Realistic applications of matrix completion in recommender systems must address several challenges that are too often neglected: (i) the discrete nature of rating-scale data, (ii) the presence of malicious users who manipulate the system to their advantage through the creation of fake profiles, and (iii) missing-not-at-random patterns, where users are more likely to rate items they expect to enjoy. Our goal in this paper is twofold. First, we propose a novel matrix completion method, robust discrete matrix completion (RDMC), designed specifically to handle the discrete nature of sparse rating-scale data and to remain reliable in the presence of adversarial manipulation. We evaluate RDMC through carefully designed experiments and realistic case studies. Our work therefore, secondly, offers a statistically-sound blueprint for future studies on how to evaluate matrix completion methods for recommender systems under realistic scenarios.

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