What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals
User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems. While recent work has explored the use of large language models (LLMs) for recommendation, it remains unclear whether LLMs can effectively reason over this type of collaborative information. In this paper, we conduct a systematic comparison between LLMs and classical matrix factorization (MF) models to assess LLMs' ability to leverage user-item interaction data. We further introduce a simple retrieval-augmented generation (RAG) method that enhances LLMs by grounding their predictions in structured interaction data. Our experiments reveal that current LLMs often fall short in capturing collaborative patterns inherent to MF models, but that our RAG-based approach substantially improves recommendation quality-highlighting a promising direction for future LLM-based recommenders.
Code (0)
등록된 구현이 없습니다.
Tasks
RAGRecommendation SystemsRetrieval-augmented GenerationSimilar Papers 제목 키워드 기반
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features describe what appears on the screen but m…
Aligning Large Language Models with Recommendation Knowledge
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like retrieval. We attribute this to a mismat…
AttributeRecommendation SystemsRetrievalMind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the "c…
Multi-Task LearningLLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) demonstrates great value in alleviating outdated knowledge or hallucination by supplying LLMs with updated and relevant knowledge. However, there are still several difficulties for RA…
HallucinationRAGRe-RankingRetrieval+2Planning and Editing What You Retrieve for Enhanced Tool Learning
Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods…
Mathematical ReasoningRetrieval