Space efficiency in group recommendation
The VLDB Journal — The International Journal on Very Large Data Bases
Double-sided recommendations: a novel framework for recommender systems
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Applying latent semantic analysis to tag-based community recommendations
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Understanding online groups through social media
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Online social networking has become a part of our everyday lives, and one of the popular online social network (SN) sites on the Internet is Facebook, where users communicate with their friends, join to groups, create groups, play games, and make friends around the world. Also, the vast number of groups are created for different causes and beliefs. However, overwhelming number of groups in one category causes difficulties for users to select a right group to join. To solve this problem, we introduce group recommendation system (GRS) using combination of hierarchical clustering technique and decision tree. We believe that Facebook SN groups can be identified based on their members' profiles. Number of experiment results showed that GRS can make 73% accurate recommendation.