# Adaptive Candidate Retrieval with Dynamic Knowledge Graph Construction for Cold-Start Recommendation

arXiv: https://arxiv.org/abs/2505.20773 · HTML: https://mlsanity.com/paper/2505.20773/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2025.

## Built on by (most cited first, 4 of 4)

- [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025)
- [LLM Reasoning for Cold-Start Item Recommendation](https://mlsanity.com/paper/2511.18261/) (2025)
- [Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations](https://mlsanity.com/paper/2604.16318/) (2026)
- [Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)](https://mlsanity.com/paper/2603.03309/) (2026)

## Abstract (arXiv)

The cold-start problem remains a critical challenge in real-world recommender systems, as new items with limited interaction data or insufficient information are frequently introduced. Despite recent advances leveraging external knowledge such as knowledge graphs (KGs) and large language models (LLMs), recommender systems still face challenges in practical environments. Static KGs are expensive to construct and quickly become outdated, while LLM-based methods depend on pre-filtered candidate lists due to limited context windows. To address these limitations, we propose ColdRAG, a retrieval-augmented framework that dynamically constructs a knowledge graph from raw metadata, extracts entities and relations to construct an updatable structure, and introduces LLM-guided multi-hop reasoning at inference time to retrieve and rank candidates without relying on pre-filtered lists. Experiments across multiple benchmarks show that ColdRAG consistently outperforms strong seven baselines.