As both the volume of data and the diversity of users accessing them increase, personalization systems offer through preferences a useful means towards improving the relevance of the query results to the information needs of the specific user posing the query. In this talk, we will focus on enhancing preferences with context. Context expresses conditions on situations external to the database. It is modeled using a set of hierarchical attributes, thus allowing context specification at various levels of detail. We will formulate the context resolution problem as the problem of selecting appropriate preferences based on context for personalizing a query. We will also present algorithms for context resolution based on data structures that index preferences by exploiting the hierarchical nature of the context attributes. Finally, we will explore the integration of preferences to achieve personalized ranked retrieval (i) in keyword search in database systems and (ii) in publish/subscribe data delivery.
Kostas Stefanidis is currently a Marie Curie fellow, funded by the ERCIM Alain Bensoussan Network, at the Data and Information Management Group of Prof. Kjetil Norvag at the Department of Computer and Information Science of the Norwegian University of Science and Technology. Previously, he worked at the database research group of Prof. Yufei Tao at the Department of Computer Science and Engineering of the Chinese University of Hong Kong. He obtained his PhD in Computer Science from the University of Ioannina, Greece, in 2009, under the supervision of Prof. Evaggelia Pitoura. He received his MSc and BSc Degrees from the same university in 2005 and 2003, respectively. His research interests include personalized search, recommendations, keyword search and social networks.
