Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A day in the life of web searching: an exploratory study
Information Processing and Management: an International Journal
Hourly analysis of a very large topically categorized web query log
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Defining a session on Web search engines: Research Articles
Journal of the American Society for Information Science and Technology
Understanding the relationship between searchers' queries and information goals
Proceedings of the 17th ACM conference on Information and knowledge management
Nereau: a social approach to query expansion
Proceedings of the 10th ACM workshop on Web information and data management
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM 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
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Narrowing down the context in the ranking phase of information retrieval has been shown to produce results that are more relevant to searcher's need. We have identified two types of contexts that could be used in the process of personalisation. We research these contexts in the domain of personalised search, but show that our approach can be used for any kind of personalisation or recommendation. We focus on two aspects of the context: temporal context and activity-based context and describe a more general personalisation framework based on lightweight semantics, that can leverage any type of context.