MemRec: Collaborative Memory-Augmented Agentic Recommender System

2026-01-13

AI Summary

This work addresses the limitations of existing agent-based recommender systems, which suffer from isolated memory and struggle to effectively leverage user collaborative signals and graph-structured context. To overcome this, we propose MemRec, a novel framework that decouples reasoning from memory management for the first time. MemRec employs a lightweight language model (LM_Mem) to dynamically maintain a collaborative memory graph and supplies high-signal contextual information to a large recommendation model (LLM_Rec). By integrating efficient retrieval with an asynchronous graph propagation algorithm, our approach enables local, open-source deployment while preserving user privacy and minimizing computational cost. Extensive experiments on four benchmark datasets demonstrate state-of-the-art performance, establishing a new Pareto frontier that balances recommendation quality, efficiency, and privacy preservation.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Mechanism Design
• Machine Learning: Graph-based Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The evolution of recommender systems has shifted preference storage from rating matrices and dense embeddings to semantic memory in the agentic era. Yet existing agents rely on isolated memory, overlooking crucial collaborative signals. Bridging this gap is hindered by the dual challenges of distilling vast graph contexts without overwhelming reasoning agents with cognitive load, and evolving the collaborative memory efficiently without incurring prohibitive computational costs. To address this, we propose MemRec, a framework that architecturally decouples reasoning from memory management to enable efficient collaborative augmentation. MemRec introduces a dedicated, cost-effective LM_Mem to manage a dynamic collaborative memory graph, serving synthesized, high-signal context to a downstream LLM_Rec. The framework operates via a practical pipeline featuring efficient retrieval and cost-effective asynchronous graph propagation that evolves memory in the background. Extensive experiments on four benchmarks demonstrate that MemRec achieves state-of-the-art performance. Furthermore, architectural analysis confirms its flexibility, establishing a new Pareto frontier that balances reasoning quality, cost, and privacy through support for diverse deployments, including local open-source models. Code:https://github.com/rutgerswiselab/memrec and Homepage: https://memrec.weixinchen.com

Problem

• recommender system
• collaborative memory
• agentic AI
• memory augmentation
• graph context

Innovation

• collaborative memory
• memory-augmented recommendation
• architectural decoupling
• asynchronous graph propagation
• large language model