Jiancan Wu

Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning

2025 · arXiv (Cornell University)

Recent advances have applied large language models (LLMs) to sequential recommendation, leveraging their pre-training knowledge and reasoning capabilities to provide more personalized user experiences. However, existing LLM-based methods fail to sufficiently leverage the rich temporal …

Self-supervised Graph Learning for Recommendation

2021

Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage …