Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation 事件

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation arXiv:2508.10312v2 Announce Type: replace Abstract: 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. When taking pretrained collaborative ID embeddings as input, LLM-based r