Showing cs.IRShow all
3 papers · 1 filter
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★ 1 cited
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…