Cloud-Device Collaborative Agents for Sequential Recommendation

• URL: http://arxiv.org/abs/2509.01551v1
• Date: Mon, 01 Sep 2025 15:28:11 GMT
• Title: Cloud-Device Collaborative Agents for Sequential Recommendation
• Authors: Jing Long, Sirui Huang, Huan Huo, Tong Chen, Hongzhi Yin, Guandong Xu,
• Abstract summary: Large language models (LLMs) have enabled agent-based recommendation systems with strong semantic understanding and flexible reasoning capabilities.<n>LLMs offer powerful personalization, but they often suffer from privacy concerns, limited access to real-time signals, and scalability bottlenecks.<n>We propose a novel Cloud-Device collaborative framework for sequential Recommendation, powered by dual agents.
• Score: 36.05863003744828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have enabled agent-based recommendation systems with strong semantic understanding and flexible reasoning capabilities. While LLM-based agents deployed in the cloud offer powerful personalization, they often suffer from privacy concerns, limited access to real-time signals, and scalability bottlenecks. Conversely, on-device agents ensure privacy and responsiveness but lack the computational power for global modeling and large-scale retrieval. To bridge these complementary limitations, we propose CDA4Rec, a novel Cloud-Device collaborative framework for sequential Recommendation, powered by dual agents: a cloud-side LLM and a device-side small language model (SLM). CDA4Rec tackles the core challenge of cloud-device coordination by decomposing the recommendation task into modular sub-tasks including semantic modeling, candidate retrieval, structured user modeling, and final ranking, which are allocated to cloud or device based on computational demands and privacy sensitivity. A strategy planning mechanism leverages the cloud agent's reasoning ability to generate personalized execution plans, enabling context-aware task assignment and partial parallel execution across agents. This design ensures real-time responsiveness, improved efficiency, and fine-grained personalization, even under diverse user states and behavioral sparsity. Extensive experiments across multiple real-world datasets demonstrate that CDA4Rec consistently outperforms competitive baselines in both accuracy and efficiency, validating its effectiveness in heterogeneous and resource-constrained environments.

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