4 papers
MindRec: A Diffusion-driven Coarse-to-Fine Paradigm for Generative Recommendation
Mengyao Gao, Chongming Gao, Haoyan Liu +5
Recent advancements in large language model-based recommendation systems often represent items as text or semantic IDs and generate recommendations in an auto-regressive manner. Ho…
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen +5
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this…
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer
Chongming Gao, Kexin Huang, Ziang Fei +6
Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. C…
Position-aware Graph Transformer for Recommendation
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have adva…