Towards Reliable Recommender Systems for Rating Data

Recommender systems are widely used in the digital landscape to match users with content fitting their preferences.

However, growing concerns about fake accounts, strategic manipulation, and other deceptive online behavior place increasing pressure on the reliability of these systems.

A common statistical approach behind recommender systems is so-called matrix completion, which predicts how users would rate items they have not yet consumed based on patterns in observed ratings.

Realistically applying matrix completion methods requires jointly addressing several overlooked challenges: (i) ratings on discrete scales (such as 1--5 stars); (ii) the presence of malicious users who deliberately manipulate the system to their advantage through fake profiles; (iii) ratings missing not at random since users are more likely to consume items they expect to like; and (iv) fostering transparency, reproducibility, and stability.

We jointly address these challenges by proposing a novel method, Robust Discrete Matrix Completion (RDMC), designed to capture the key characteristics of sparse rating data while remaining reliable in the presence of manipulation.

We evaluate RDMC through two case studies and carefully designed simulation experiments.

Our work thereby offers a statistically-sound blueprint for future studies on how to evaluate recommender systems under realistic scenarios.