摘要翻译：
随着信息量和选择数量的增加，搜索和决策信息变得越来越困难。推荐系统帮助用户找到特定类型的感兴趣的项目，如电影或餐馆，但使用起来仍然有些尴尬。我们的解决方案是利用个性化推荐系统和对话系统的互补优势，创建个性化助手。我们提出了一个系统--Adaptive Place Advisor--它将项目选择视为一个交互式的对话过程，程序查询项目属性，用户进行响应。个人的、长期的用户偏好是在正常的推荐对话过程中不引人注目地获得的，并用于指导未来与同一用户的对话。我们提出了一个新的用户模型，它影响项目搜索和对话中问的问题。我们证明了我们的系统的有效性，与对照组的用户与非自适应版本的系统交互相比，我们的系统在显著减少找到满意项目所需的交互时间和数量方面的有效性。
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英文标题：
《A Personalized System for Conversational Recommendations》
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作者：
M. H. Goker, P. Langley, C. A. Thompson
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.
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PDF链接：
https://arxiv.org/pdf/1107.0029