2 papers
cs.IR2026
PreferRec: Learning and Transferring Pareto Preferences for Multi-objective Re-ranking
Wei Zhou, Wuyang Li, Junkai Ji +5
Multi-objective re-ranking has become a critical component of modern multi-stage recommender systems, as it tasked to balance multiple conflicting objectives such as accuracy, dive…
cs.NE2026
An Efficient Evolutionary Algorithm for Few-for-Many Optimization
Ke Shang, Hisao Ishibuchi, Zexuan Zhu +1
Few-for-many (F4M) optimization, recently introduced as a novel paradigm in multi-objective optimization, aims to find a small set of solutions that effectively handle a large numb…