# Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems

arXiv: https://arxiv.org/abs/2502.14137 · HTML: https://mlsanity.com/paper/2502.14137/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2025.

## Built on by (most cited first, 16 of 16)

- [MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation](https://mlsanity.com/paper/2510.07713/) (2025)
- [Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts](https://mlsanity.com/paper/2509.26093/) (2025)
- [A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges](https://mlsanity.com/paper/2507.21117/) (2025)
- [Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation](https://mlsanity.com/paper/2605.28175/) (2026)
- [Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders](https://mlsanity.com/paper/2505.23053/) (2025)
- [Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation](https://mlsanity.com/paper/2510.05125/) (2025)
- [LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation](https://mlsanity.com/paper/2503.10703/) (2025)
- [LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems](https://mlsanity.com/paper/2507.04722/) (2025)
- [On the Mechanisms of Collaborative Learning in VAE Recommenders](https://mlsanity.com/paper/2511.06781/) (2025)
- [SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems](https://mlsanity.com/paper/2603.03536/) (2026)
- [The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification](https://mlsanity.com/paper/2604.18728/) (2026)
- [RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation](https://mlsanity.com/paper/2605.07129/) (2026)
- [Interval Certifications for Multilayered Perceptrons via Lattice Traversal](https://mlsanity.com/paper/2607.08773/) (2026)
- [Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation](https://mlsanity.com/paper/2608.06632/) (2026)
- [Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation](https://mlsanity.com/paper/2608.15949/) (2026)
- [AgentWebRec: Compact Evidence Fusion over the Agent Web for Personalized Recommendation](https://mlsanity.com/paper/2610.01705/) (2026)

## Abstract (arXiv)

Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their superior understanding of context-aware user preferences, they typically struggle to leverage behavioral data, which have proven to be important for classical collaborative filtering (CF)-based approaches. For this reason, we propose CRAG, Collaborative Retrieval Augmented Generation for LLM-based CRS. To the best of our knowledge, CRAG is the first approach that combines state-of-the-art LLMs with CF for conversational recommendations. Our experiments on two publicly available movie conversational recommendation datasets, i.e., a refined Reddit dataset (which we name Reddit-v2) as well as the Redial dataset, demonstrate the superior item coverage and recommendation performance of CRAG, compared to several CRS baselines. Moreover, we observe that the improvements are mainly due to better recommendation accuracy on recently released movies. The code and data are available at https://github.com/yaochenzhu/CRAG.