3 papers
cs.IR2026
Turning Semantics into Topology: LLM-Driven Attribute Augmentation for Collaborative Filtering
Junjie Meng, Ranxu zhang, Wei Wu +6
Large Language Models (LLMs) have shown great potential for enhancing recommender systems through their extensive world knowledge and reasoning capabilities. However, effectively t…
cs.IR2024
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +4
Nowadays, many recommender systems encompass various domains to cater to users' diverse needs, leading to user behaviors transitioning across different domains. In fact, user behav…
cs.IR2024
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +5
Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one i…