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报告人： 宋睿华 微软小冰首席科学家 微软(亚洲)互联网工程院
摘要： 与众多厂商投入问答或任务型对话不同，微软小冰选择深耕细作闲聊领域。有人认为，闲聊没有显而易见的用处，而我却被这种好玩儿的对话深深吸引。在这次讲座中，我想跟大家介绍小冰在最近一年里从模仿到创造再到多模态理解的一些成果，希望给大家展示一些机器学习能做的好玩儿的应用。今天，小冰已不仅是一个聊天机器人，它所代表的情感计算框架涵盖了长程对话、人工智能创造和多模态等多方面的研究课题，支撑着未来塑造不同类型和性格的AI beings（硅基人类）。
Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.