论文标题
深度对话推荐系统：面向目标对话系统的新边界
Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
论文作者
论文摘要
近年来，利用自然语言处理技术的推荐系统的新兴主题引起了很多关注，其应用之一是对话推荐系统（CRS）。与传统的推荐系统采用基于内容和协作的过滤方法不同，CRS通过交互式对话对话来学习和模型用户的偏好。在这项工作中，我们提供了CRS最近演变的总结，其中深度学习方法被应用于CRS并产生了富有成果的结果。我们首先分析了研究问题，并提出了深度对话推荐系统（DCRS）的关键挑战，然后介绍了最新研究的当前现场状态，包括使DCR受益的最常见的深度学习模型。最后，我们讨论了这个充满活力的地区的未来方向。
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS). Unlike traditional recommender systems with content-based and collaborative filtering approaches, CRS learns and models user's preferences through interactive dialogue conversations. In this work, we provide a summarization of the recent evolution of CRS, where deep learning approaches are applied to CRS and have produced fruitful results. We first analyze the research problems and present key challenges in the development of Deep Conversational Recommender Systems (DCRS), then present the current state of the field taken from the most recent researches, including the most common deep learning models that benefit DCRS. Finally, we discuss future directions for this vibrant area.
