9 papers
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…
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…
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…
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
Hexiang Tan, Fei Sun, Sha Liu +8
As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness. However, existing detection…
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…