Kezhi Lu

PARS: Partial-Label-Learning-inspired Recommender Systems

Recommender systems are widely required and deployed to address real-world problems. In this paper, we study a new yet challenging real-world setting for recommender systems, where only user browsing histories are available without any explicit feedback. No item acquisition information, e.g., purcha