Towards recommendation to trust-based user groups in social tagging systems

Group recommender systems use various strategies to aggregate users' preferences into a common social welfare function which would maximize the satisfaction of all members. Group recommendation is essentially useful for websites, especially for social tagging systems. In this paper, we initially experiment with various rank aggregation strategies for group recommendation in social tagging systems. Specially, we consider trust-based user groups detected by community discovery based on trustable social relations. Also, we present hybrid similarity to estimate the relevance between users and resources. According to experiments on Delicious and Lastfm datasets, CombMAX, CombSUM and CombANZ are more suitable for aggregating individual preference into a group preference in social tagging systems. And group recommendation can achieve better effect than individual recommendation based on our proposed model.