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cs.IR2025
The 2nd Workshop on Human-Centered Recommender Systems
Kaike Zhang, Jiakai Tang, Du Su +6
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and…
cs.IR2025
GoalRank: Group-Relative Optimization for a Large Ranking Model
Kaike Zhang, Xiaobei Wang, Shuchang Liu +7
Mainstream ranking approaches typically follow a Generator-Evaluator two-stage paradigm, where a generator produces candidate lists and an evaluator selects the best one. Recent wo…
cs.IR2025
From Generation to Consumption: Personalized List Value Estimation for Re-ranking
Kaike Zhang, Xiaobei Wang, Xiaoyu Yang +5
Re-ranking is critical in recommender systems for optimizing the order of recommendation lists, thus improving user satisfaction and platform revenue. Most existing methods follow…