most citedLANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.IR2024★ 3 cited

LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation

Hongke Zhao, Songming Zheng, Likang Wu +2

The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensiv…

cs.IR2024

Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning

Zhong Guan, Likang Wu, Hongke Zhao +2

Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task…