2 papers
cs.IR2026
Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation
Fenglin Yan, Bohao Wang, Jian Zhang +5
Large language models (LLMs) have been widely adopted as backbones for recommender systems. However, their language-centric pretraining makes it difficult to capture collaborative…
cs.IR2026
IMFuse: Instance-Aware Multi-Layer Fusion for LLM-Enhanced Sequential Recommendation
Yuheng Zheng, Yu Cui, Bin Wu +4
Recent advancements in Large Language Models (LLMs) have significantly enhanced sequential recommendation by encoding rich item textual information into semantic representations. H…