AI RESEARCH
Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
arXiv CS.CL
•
ArXi:2508.10312v2 Announce Type: replace Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history.