Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Supporting social recommendations with activity-balanced clustering
Proceedings of the 2007 ACM conference on Recommender systems
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Data mining for web personalization
The adaptive web
Computers in Human Behavior
Putting humans in the loop: Social computing for Water Resources Management
Environmental Modelling & Software
SoBot: facilitating conversation using social media data and a social agent
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).