Evaluating similarity measures: a large-scale study in the orkut social network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
WMR--A Graph-Based Algorithm for Friend Recommendation
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Who interacts on the Web?: The intersection of users' personality and social media use
Computers in Human Behavior
A cultural knowledge-based method to support the formation of homophilous online communities
CHI '11 Extended Abstracts on Human Factors in Computing Systems
A Framework of Recommendation System Based on Both Network Structure and Messages
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Finding and Matching Communities in Social Networks Using Data Mining
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Interest-based real-time content recommendation in online social communities
Knowledge-Based Systems
Community focused social network extraction
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Social networking in developing regions
Proceedings of the Fifth International Conference on Information and Communication Technologies and Development
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Social networking sites have grown very recently, allowing the formation and maintenance of online communities. We believe online communities formed by homophile people tend to have more active members. Homophily refers to the degree of similarity between individuals regarding beliefs, socioeconomic status, education, personal preferences, and so on. We describe here a method to identify people who are potentially homophile, ie social networking users who share several common themes, to optimize the proposed method Sack-of-Semantics (SoS). The first version of the SoS method was developed considering existig recommender systems, coupled to cultural translation from OMCS-Br knowledgebase to identify people talking about the same subject in a social network considering their cultural contexts. So even if the express themselves differently, with terms of his own cultural context, they can be identified because, using cultural translation, it is possible to see that they talk about the same subject. This second version of the SoS considers not only a subject, but many subjects to do the search and recommendation among people, fostering networking homophile.