用自适应多智能体协作提升推荐系统对话能力
AgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence
- 分层智能体分工协作，动态理解用户需求
- 对话成功率提升2.8%，推荐准确率提高1.9%
- 适合需要自然语言交互的智能推荐场景
互动式对话推荐系统因其能通过自然语言捕捉用户偏好而受到关注。然而，现有方法在应对动态偏好、保持对话连贯性以及同时平衡多个排序目标方面仍面临挑战。本文提出AgentRec，一种基于大模型的多智能体协同推荐框架，通过具有自适应智能的分层代理网络解决这些问题。该方法采用专用大模型驱动的智能体，分别负责对话理解、偏好建模、上下文感知和动态排序，并通过自适应加权机制学习交互模式进行协调。我们设计了三层学习策略：针对简单查询快速响应，复杂偏好智能推理，疑难场景深度协作。在三个真实数据集上的实验表明，AgentRec在主流基线基础上实现持续提升：对话成功率达2.8%提升，推荐准确率（NDCG@10）提高1.9%，对话效率改善3.2%，且计算成本相当。
原文摘要 · Abstract (English)
Interactive conversational recommender systems have gained significant attention for their ability to capture user preferences through natural language interactions. However, existing approaches face substantial challenges in handling dynamic user preferences, maintaining conversation coherence, and balancing multiple ranking objectives simultaneously. This paper introduces AgentRec, a next-generation LLM-powered multi-agent collaborative recommendation framework that addresses these limitations through hierarchical agent networks with adaptive intelligence. Our approach employs specialized LLM-powered agents for conversation understanding, preference modeling, context awareness, and dynamic ranking, coordinated through an adaptive weighting mechanism that learns from interaction patterns. We propose a three-tier learning strategy combining rapid response for simple queries, intelligent reasoning for complex preferences, and deep collaboration for challenging scenarios. Extensive experiments on three real-world datasets demonstrate that AgentRec achieves consistent improvements over state-of-the-art baselines, with 2.8\% enhancement in conversation success rate, 1.9\% improvement in recommendation accuracy (NDCG@10), and 3.2\% better conversation efficiency while maintaining comparable computational costs through intelligent agent coordination.
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