GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Accounting for taste: using profile similarity to improve recommender systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On-line personalized sales promotion in electronic commerce
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
MAPIS, a multi-agent system for information personalization
Information and Software Technology
Expansion of telecommunication social networks
CDVE'07 Proceedings of the 4th international conference on Cooperative design, visualization, and engineering
Universal Navigation through Social Networking
OCSC '09 Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009
Personalized book recommendations created by using social media data
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
A personalized recommendation system on scholarly publications
Proceedings of the 20th ACM international conference on Information and knowledge management
Factors influencing mobile advertising avoidance
International Journal of Mobile Communications
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Most of recommendation mechanisms have been attempting to identify a set of cohesive user groups (or clusters) of which members in the same group might be interested in a common area (e.g., movies and musics) than others. Practically, this assumption is not working well, because the statistical analysis to extract simple demographic features (e.g., ages, genders, and so on) can not find out personal context in a certain situation, i.e., a more specific recommendation for each person. In order to solve this problem, we want to take into account social relations (particularly, kin relations) to identify each person. As aggregating the social networks, we can build a social network for making various social relations extractable. Most importantly, we are introducing our experiences on discovering social networks for providing personalized mobile services. Real customer information has been provided from KT Freetel (KTF), one of the major telecommunication companies in Korea. This work is an on-going research project for delivering personalized information to mobile devices via the social networks.