Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the 18th international conference on World wide web
Exploiting internal and external semantics for the clustering of short texts using world knowledge
Proceedings of the 18th ACM conference on Information and knowledge management
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Exploring online social activities for adaptive search personalization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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A user's context in work environments, or work context, provides finegrained knowledge on the user's skills, projects, and collaborators. Such work context is valuable to personalize many web applications, such as search and various recommendation tasks. In this paper, we explore the use of work contexts derived from users' various online social activities, such as tagging and blogging, for personalization purposes. We describe a system for building user work context profiles, including methods for cleaning source data, integrating information from multiple sources, and performing semantic enrichment on user data. We have evaluated the quality of the created work-context profiles through simulations on personalizing two common web applications, namely tag recommendation and search, using real-world data collected from large-scale social systems.