5 papers
Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems
Qiyao Ma, Menglin Yang, Mingxuan Ju +3
Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balance content exploration and exploitatio…
FlexRec: Adapting LLM-based Recommenders for Flexible Needs via Reinforcement Learning
Yijun Pan, Weikang Qiu, Qiyao Ma +4
Modern recommender systems must adapt to dynamic, need-specific objectives for diverse recommendation scenarios, yet most traditional recommenders are optimized for a single static…
MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale
Ya Wen, Jixuan Cai, Qiyao Ma +4
Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging philosophies and techniques. Whil…
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
Menglin Yang, Jialin Chen, Jinkai Tao +9
The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advan…
XRec: Large Language Models for Explainable Recommendation
Qiyao Ma, Xubin Ren, Chao Huang
Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adop…