IAI MovieBot 2.0：具有可训练神经组件与透明用户建模的增强型研究平台
IAI MovieBot 2.0: An Enhanced Research Platform with Trainable Neural Components and Transparent User Modeling
AI总结：
针对缺乏可支撑综合研究的对话式推荐平台问题，本文推出 IAI MovieBot 2.0，通过可训练神经组件、透明可解释的用户偏好建模及界面与基础设施改进，提供面向用户实验的增强研究平台。
摘要：
尽管人们对对话式推荐系统的兴趣不断上升，但目前仍缺乏适合作为综合研究平台的运行系统。本文介绍了对话式电影推荐系统 IAI MovieBot 的增强版本，旨在将其发展为一个稳健、适应性强的平台，用于开展面向用户的实验。此次增强的主要亮点包括：新增用于自然语言理解和对话策略的可训练神经组件，对用户偏好进行透明且可解释的建模，以及用户界面和研究基础设施方面的改进。
英文摘要：
While interest in conversational recommender systems has been on the rise, operational systems suitable for serving as research platforms for comprehensive studies are currently lacking. This paper introduces an enhanced version of the IAI MovieBot conversational movie recommender system, aiming to evolve it into a robust and adaptable platform for conducting user-facing experiments. The key highlights of this enhancement include the addition of trainable neural components for natural language understanding and dialogue policy, transparent and explainable modeling of user preferences, along with improvements in the user interface and research infrastructure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
