Proceedings of the 11th international conference on World Wide Web
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Discovering and using groups to improve personalized search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
I tag, you tag: translating tags for advanced user models
Proceedings of the third ACM international conference on Web search and data mining
An optimization framework for query recommendation
Proceedings of the third ACM international conference on Web search and data mining
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Disambiguating search by leveraging a social context based on the stream of user's activity
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Extracting semantic user networks from informal communication exchanges
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Reducing the sparsity of contextual information for recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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In this paper we present an approach to contextual search, based on the automatically extracted metadata from visited documents. User model represents user's interests as a combination of tags, keywords and named entities. Such user model is further enhanced by automatically detected communities of similar users, based on the similarities of their models. The user may belong to multiple communities, each representing one of her possibly many personas - roles or stereotypes, facets of her interests. We discuss further possibilities of using this model to bring more fine-grained contextualization and search improvement by using short contexts.