3 papers
cs.LG2026
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Yun Lu, Xiaoyu Shi, Hong Xie +2
Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairne…
cs.AI2025
Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knob
Yun Lu, Xiaoyu Shi, Hong Xie +3
This paper revisits fairness-aware interactive recommendation (e.g., TikTok, KuaiShou) by introducing a novel control knob, i.e., the lifecycle of items. We make threefold contribu…
cs.SI2016
Identifying emerging influential Nodes in evolving networks: Exploiting strength of weak nodes
Khushnood Abbas, Mingsheng Shang, Cai Shi-Min +1
Identifying emerging influential or popular node/item in future on network is a current interest of the researchers. Most of previous works focus on identifying leaders in time evo…