【摘要】 群组推荐在信息检索与数据挖掘领域近年来备受关注,其旨在从海量候选集中挑选出一组用户可能感兴趣的项目.随着深度学习技术的不断发展,基于深度学习的群组推荐方法大量涌现.鉴于此,首先介绍群组推荐问题的背景知识,然后系统综述数据获取方法,全面评述近年来基于深度学习的群组推荐算法,并进行系统分类与深入分析.此外,还归纳了适用于深度学习方法的群组推荐数据集和评价方法,对各类推荐算法进行对比实验分析与讨论.最后,针对本领域的研究难点进行深入探讨,并提出未来有待深入研究的方向.更多还原

【Abstract】 Group recommendation has emerged as a highly active research topic in the fields of information retrieval and data mining in recent years. Its objective is to select a group of items from a large candidate set that is likely to be of interest to a set of users. With the advancement of deep learning, numerous group recommendation methods based on deep learning have been proposed. This paper provides a brief introduction to the background knowledge of this problem. It reviews the methods of data acquisition and conducts a comprehensive review, systematic classification, and in-depth analysis of group recommendation algorithms based on deep learning. In addition, this paper outlines some group recommendation datasets and evaluation methods suitable for deep methods, and conducts comparative experimental analysis and discussion on various recommendation algorithms. Finally, the research challenges in this field were analyzed, and valuable future research directions were discussed.更多还原