中文摘要：

近年来,组推荐系统逐渐成为推荐系统领域的研究热点之一.大部分推荐系统主要关注单个用户的推荐,然而在许多日常活动中需要为多个用户形成的群组进行推荐.组推荐系统作为解决群组推荐问题的有效手段,将单个用户推荐扩展为群组推荐,这为推荐系统的研究带来了一些新的挑战.根据群组特征和社会化因素,融合群组成员偏好以满足所有群组成员的偏好需求是组推荐系统的主要任务.该文对最近几年组推荐系统研究进展进行综述,从组推荐系统的形式化定义和研究框架入手,对组推荐系统的用户偏好获取、群组发现、偏好融合算法、社会化组推荐以及效用评价等关键技术进行前沿概况,并分析了群组特征对偏好融合算法的影响.对组推荐系统在不同领域的应用进展进行归纳和总结.最后,对组推荐系统有待深入研究的难点和发展方向进行展望.

英文摘要：

Group recommender systems have recently become one of the hottest topics in domain of recommender systems.While most recommender systems are focused on making recommendations to individual users,recommendations for a group of users are necessary in many daily activities.Group recommender systems as an effective solution to the problem of group recommendation incurs some new challenges for recommender systems research by extending individual users recommendation to group recommendation.The main task of group recommender systems is to satisfy preferences of all group members by aggregating preferences of group members with group features and social factors.This paper introduces formal definition and a framework of group recommendation,and then presents an overview of the field of group recommender systems including preference elicitation,group detection,preference aggregation algorithms,social group recommendation,evaluation and typical applications in different domains.In addition,the paper discusses the influence of group features on effectiveness of preference aggregation algorithms.Finally,the prospects for future development and suggestions for possible extensions are also discussed.