Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
How accurately can one's interests be inferred from friends
Proceedings of the 19th international conference on World wide web
Scholarly paper recommendation via user's recent research interests
Proceedings of the 10th annual joint conference on Digital libraries
User-centric query refinement and processing using granularity-based strategies
Knowledge and Information Systems
Research interests: their dynamics, structures and applications in unifying search and reasoning
Journal of Intelligent Information Systems
User interests modeling based on multi-source personal information fusion and semantic reasoning
AMT'11 Proceedings of the 7th international conference on Active media technology
Ranking and combining social network data for web personalization
Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media
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User related data indicate user interests in a certain environment. In the context of massive data from the Web, if an application wants to provide more personalized service (e.g. search) for users, an investigation on user interests is needed. User interests are usually distributed in different sources. In order to provide a more comprehensive understanding, user related data from multiple sources need to be integrated together for deeper analysis. Web based social networks have become typical platforms for extracting user interests. In addition, there are various types of interests from these social networks. In this paper, we provide an algorithmic framework for retrieving semantic data based on user interests from multiple sources (such as multiple social networking sites). We design several algorithms to deal with interests based retrieval based on single and multiple types of interests. We utilize publication data from Semantic Web Dog Food (which can be considered as an academic collaboration based social network), and microblogging data from Twitter to validate our framework. The Active Academic Visit Recommendation Application (AAVRA) is developed as a concrete usecase to show the potential effectiveness of the proposed framework for user interests driven Web personalization based on multiple social networks.