Dual-Regularized One-Class Collaborative Filtering
Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw Karol Szymanski, Jian Lü · 2014
Collaborative filtering is a fundamental building block in many recommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples.