Beibei Kong — accepted AI-conference papers

2026

G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation

Boyu Chen, Siran Chen, Zhengrong Yue, Kainan Yan, Chenyun Yu, Beibei Kong +4

AAAI 2026technical

User feedback is critical for refining recommendation systems, yet explicit feedback (e.g., likes or dislikes) remains scarce in practice. As a more feasible alternative, inferring user preferences from massive implicit feedback has shown great potential (e.g., a user quickly skipping a recommended

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2022

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen +6

NeurIPS 2022accept

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publ…

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