Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Research interests: their dynamics, structures and applications in unifying search and reasoning
Journal of Intelligent Information Systems
Interest logic and its application on the web
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
User interests driven web personalization based on multiple social networks
Proceedings of the 4th International Workshop on Web Intelligence & Communities
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Various Web-based social network data reflect user interests from multiple perspectives in a distributed environment. They need to be integrated for better user modelling and personalized services. We argue that in different scenarios, different social networks play different roles and their degrees of importance are not equivalent. Hence, ranking strategies among different social network data sources are needed. In addition, combining different social network data can produce interesting subsets of these data with different levels of importance. In this paper, we propose social network data ranking and composition strategies, we validate the proposed methods by collaboration network data (Semantic Web Dog Food) and micro-blogging data (from Twitter), then we use the ranked and composed results for developing a Web-based personalized academic visit recommendation system to show their potential effectiveness.