Low-Rank Learning for Transparent Recommender Systems

This comprehensive monograph explores advanced concepts in recommender systems, focusing on sparse feedback, adaptive regularization, and nonnegative factorization. Ideal for researchers, data scientists, and mathametics professionals, it offers clear explanations and practical insights into building transparent and effective recommendation algorithms. Elevate your expertise with this authoritative resource.