Uncertainty-Adjusted Recommendation via Matrix Factorization With Weighted Losses

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 11; pp. 15624 - 15637
• Main Authors: Alves, Rodrigo, Ledent, Antoine, Kloft, Marius
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2024
• Subjects: Adaptation models; Behavioral sciences; Complexity theory; Learning systems; Matrix completion; Noise measurement; Predictive models; recommender systems (RSs); Uncertainty; uncertainty estimation; uncertainty-aware learning

In a recommender systems (RSs) dataset, observed ratings are subject to unequal amounts of noise. Some users might be consistently more conscientious in choosing the ratings they provide for the content they consume. Some items may be very divisive and elicit highly noisy reviews. In this article, w...