10 citations · 15 across the 5 of their papers we have counts for
5 papers
LLM-Powered User Simulator for Recommender System
Zijian Zhang, Shuchang Liu, Ziru Liu +6
User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerati…
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future
Shilin Sun, Wenbin An, Feng Tian +5
Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challe…
GPRec: Bi-level User Modeling for Deep Recommenders
Yejing Wang, Dong Xu, Xiangyu Zhao +7
GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a divers…
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao +6
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analys…
Diffusion Augmentation for Sequential Recommendation
Qidong Liu, Fan Yan, Xiangyu Zhao +4
Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions…