With the rise of smart personal devices, service-oriented human-agent interactions have become increasingly prevalent. This trend highlights the need for personalized dialogue assistants that can understand user-specific traits to accurately interpret requirements and tailor responses to individual preferences. However, existing approaches often overlook the complexities of long-term interactions and fail to capture users' subjective characteristics. To address these gaps, we present PAL-Bench, a new benchmark designed to evaluate the personalization capabilities of service-oriented assistants in long-term user-agent interactions. In the absence of available real-world data, we develop a multi-step LLM-based synthesis pipeline, which is further verified and refined by human annotators. This process yields PAL-Set, the first Chinese dataset comprising multi-session user logs and dialogue histories, which serves as the foundation for PAL-Bench. Furthermore, to improve personalized service-oriented interactions, we propose H$^2$Memory, a hierarchical and heterogeneous memory framework that incorporates retrieval-augmented generation to improve personalized response generation. Comprehensive experiments on both our PAL-Bench and an external dataset demonstrate the effectiveness of the proposed memory framework.

翻译：随着智能个人设备的兴起，面向服务的人机交互日益普遍。这一趋势凸显了对个性化对话助手的需求，这类助手需理解用户特定特征，以准确解读需求并依据个体偏好定制回应。然而，现有方法常忽视长期交互的复杂性，且未能捕捉用户的主观特征。为弥补这些不足，我们提出了PAL-Bench，这是一个旨在评估面向服务助手在长期用户-智能体交互中个性化能力的新基准。在缺乏可用真实世界数据的情况下，我们开发了一个基于大语言模型的多步骤合成流程，并经由人工标注者进一步验证与优化。该流程生成了PAL-Set，这是首个包含多会话用户日志与对话历史的中文数据集，作为PAL-Bench的基础。此外，为提升个性化服务导向的交互，我们提出了H$^2$Memory，一个分层异构的记忆框架，结合检索增强生成技术以改进个性化回应生成。在我们自建的PAL-Bench及外部数据集上的综合实验验证了所提记忆框架的有效性。