Group recommendation in social tagging systems by consistent utilization of items and tags information
Xiaofang Wang, Xiuyang Zhao, Jin Zhou, Ming Xu · 2016
Group recommendations have become an increasingly important application of social tagging systems in recent years because of the rapid development and growth of groups. In contrast to current studies on group recommendation approaches that applied users-groups binary relations or users-tags-groups ternary relations, this work proposes a novel method named 4-order tensor reduction orthogonal iteration algorithm, which can recommend groups to users based on the consistent fusion of the items and tags information that exist in social tagging system. Four types of entities (users, tags, items, and groups) are integrated into a 4-order tensor recommendation framework, and then, Higher Order Singular Value Decomposition and Higher Order Orthogonal Iteration methods are performed to reveal the latent semantic association among these entities and recommend groups to users. The results of our experiments on a Flickr dataset show that the proposed group recommendation approach outperforms certain popular methods that only based on users-groups binary relations or users-tags-groups ternary relations in terms of mean average precision (MAP).