Cold-Start Recommendation with Knowledge-Guided Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2505.20773v1
• Date: Tue, 27 May 2025 06:23:26 GMT
• Title: Cold-Start Recommendation with Knowledge-Guided Retrieval-Augmented Generation
• Authors: Wooseong Yang, Weizhi Zhang, Yuqing Liu, Yuwei Han, Yu Wang, Junhyun Lee, Philip S. Yu,
• Abstract summary: ColdRAG is a retrieval-augmented generation approach that builds a domain-specific knowledge graph.<n>We show that ColdRAG surpasses existing zero-shot baselines in Recall and NDCG.<n>This framework offers a practical solution to cold-start recommendation by combining knowledge-graph reasoning with retrieval-augmented LLM generation.
• Score: 33.471004805877186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cold-start items remain a persistent challenge in recommender systems due to their lack of historical user interactions, which collaborative models rely on. While recent zero-shot methods leverage large language models (LLMs) to address this, they often struggle with sparse metadata and hallucinated or incomplete knowledge. We propose ColdRAG, a retrieval-augmented generation approach that builds a domain-specific knowledge graph dynamically to enhance LLM-based recommendation in cold-start scenarios, without requiring task-specific fine-tuning. ColdRAG begins by converting structured item attributes into rich natural-language profiles, from which it extracts entities and relationships to construct a unified knowledge graph capturing item semantics. Given a user's interaction history, it scores edges in the graph using an LLM, retrieves candidate items with supporting evidence, and prompts the LLM to rank them. By enabling multi-hop reasoning over this graph, ColdRAG grounds recommendations in verifiable evidence, reducing hallucinations and strengthening semantic connections. Experiments on three public benchmarks demonstrate that ColdRAG surpasses existing zero-shot baselines in both Recall and NDCG. This framework offers a practical solution to cold-start recommendation by combining knowledge-graph reasoning with retrieval-augmented LLM generation.

Related papers

• LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking [0.0]
  We introduce LlamaRec-LKG-RAG, a novel single-pass, end-to-end trainable framework that integrates personalized knowledge graph context into recommendation ranking.<n>Our approach extends the LlamaRec architecture by incorporating a lightweight user preference module that dynamically identifies salient relation paths.<n>Experiments on ML-100K and Amazon Beauty datasets demonstrate consistent and significant improvements over LlamaRec across key ranking metrics.
  arXiv  Detail & Related papers  (2025-06-09T05:52:03Z)
• RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion [3.680772033409751]
  Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities.<n>We introduce RECIPE-TKG, a lightweight and data-efficient framework designed to improve accuracy and generalization in settings with sparse historical context.
  arXiv  Detail & Related papers  (2025-05-23T12:11:40Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• Explain What You Mean: Intent Augmented Knowledge Graph Recommender Built With An LLM [8.40367600570591]
  Intent Knowledge Recommender (IKGR) is a novel framework that leverages retrieval-augmented generation and an encoding approach to construct and densify a knowledge graph.<n>IKGR overcomes knowledge gaps and achieves substantial gains over state-of-the-art baselines on both publicly available and our internal recommendation datasets.
  arXiv  Detail & Related papers  (2025-05-16T06:07:19Z)
• Graph Retrieval-Augmented LLM for Conversational Recommendation Systems [52.35491420330534]
  G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems) is a training-free framework that combines graph retrieval-augmented generation and in-context learning.<n>G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.
  arXiv  Detail & Related papers  (2025-03-09T03:56:22Z)
• G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation [48.23263809469786]
  We propose a framework using graph retrieval-augmented large language models (LLMs) for explainable recommendation.<n>G-Refer achieves superior performance compared with existing methods in both explainability and stability.
  arXiv  Detail & Related papers  (2025-02-18T06:42:38Z)
• ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation [16.204046295248546]
  Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models.<n>We introduce a novel graph-based RAG approach, called Attributed Community-based Hierarchical RAG (ArchRAG)<n>We build a novel hierarchical index structure for the attributed communities and develop an effective online retrieval method.
  arXiv  Detail & Related papers  (2025-02-14T03:28:36Z)
• SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization [70.11167263638562]
  Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. We first present a simple yet well-crafted framework named name, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework.
  arXiv  Detail & Related papers  (2024-10-28T18:10:26Z)
• Graph Reasoning for Explainable Cold Start Recommendation [1.8434042562191815]
  The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems. We propose GRECS: a framework for adapting Graph Reasoning methods to cold start recommendations. Our experiments show that GRECS mitigates the cold start problem and outperforms competitive baselines while being explainable.
  arXiv  Detail & Related papers  (2024-06-11T16:21:57Z)
• Hierarchical Memory Learning for Fine-Grained Scene Graph Generation [49.39355372599507]
  This paper proposes a novel Hierarchical Memory Learning (HML) framework to learn the model from simple to complex. After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
  arXiv  Detail & Related papers  (2022-03-14T08:01:14Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• Privileged Graph Distillation for Cold Start Recommendation [57.918041397089254]
  The cold start problem in recommender systems requires recommending to new users (items) based on attributes without any historical interaction records. We propose a privileged graph distillation model(PGD) Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item.
  arXiv  Detail & Related papers  (2021-05-31T14:05:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.