9 citations · 10 across the 8 of their papers we have counts for
8 papers · 1 filter
Towards Faithful Simulation of Human Shopping Behavior
Jiakai Tang, Yan Mi, Jing Yu +9
Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made en…
AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
Kaike Zhang, Qi Cao, Fei Sun +3
Sequential recommender systems have demonstrated strong capabilities in modeling users' dynamic preferences and capturing item transition patterns. However, real-world user behavio…
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…
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…
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…
Personalized Denoising Implicit Feedback for Robust Recommender System
Kaike Zhang, Qi Cao, Yunfan Wu +3
While implicit feedback is foundational to modern recommender systems, factors such as human error, uncertainty, and ambiguity in user behavior inevitably introduce significant noi…