# MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models

arXiv: https://arxiv.org/abs/2407.04960 · HTML: https://mlsanity.com/paper/2407.04960/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2024.

## Built on by (most cited first, 17 of 17)

- [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026)
- [A Survey on Diffusion Models for Recommender Systems](https://mlsanity.com/paper/2409.05033/) (2024)
- [InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.15872/) (2025)
- [The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems](https://mlsanity.com/paper/2507.02097/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [Memory Assisted LLM for Personalized Recommendation System](https://mlsanity.com/paper/2505.03824/) (2025)
- [Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations](https://mlsanity.com/paper/2411.14459/) (2024)
- [Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts](https://mlsanity.com/paper/2509.26093/) (2025)
- [ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling](https://mlsanity.com/paper/2510.14703/) (2025)
- [From Atom to Community: Structured and Evolving Agent Memory for User Behavior Modeling](https://mlsanity.com/paper/2601.16872/) (2026)
- [Generative Representational Learning of Foundation Models for Recommendation](https://mlsanity.com/paper/2506.11999/) (2025)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (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)
- [Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2508.04032/) (2025)
- [USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model](https://mlsanity.com/paper/2509.20381/) (2025)
- [PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction](https://mlsanity.com/paper/2607.02881/) (2026)

## Abstract (arXiv)

Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most existing CRS models mainly focus on dialogue comprehension and preferences mining from the current dialogue session, overlooking user preferences in historical dialogue sessions. The preferences embedded in the user's historical dialogue sessions and the current session exhibit continuity and sequentiality, and we refer to CRSs with this characteristic as sequential CRSs. In this work, we leverage memory-enhanced LLMs to model the preference continuity, primarily focusing on addressing two key issues: (1) redundancy and noise in historical dialogue sessions, and (2) the cold-start users problem. To this end, we propose a Memory-enhanced Conversational Recommender System Framework with Large Language Models (dubbed MemoCRS) consisting of user-specific memory and general memory. User-specific memory is tailored to each user for their personalized interests and implemented by an entity-based memory bank to refine preferences and retrieve relevant memory, thereby reducing the redundancy and noise of historical sessions. The general memory, encapsulating collaborative knowledge and reasoning guidelines, can provide shared knowledge for users, especially cold-start users. With the two kinds of memory, LLMs are empowered to deliver more precise and tailored recommendations for each user. Extensive experiments on both Chinese and English datasets demonstrate the effectiveness of MemoCRS.