Quantum computation and quantum information
Quantum computation and quantum information
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Context modeling and discovery using vector space bases
Proceedings of the 14th ACM international conference on Information and knowledge management
Measuring online information seeking context, Part 1: Background and method
Journal of the American Society for Information Science and Technology
A study on the effects of personalization and task information on implicit feedback performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Utilizing a geometry of context for enhanced implicit feedback
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A probability ranking principle for interactive information retrieval
Information Retrieval
A basis for information retrieval in context
ACM Transactions on Information Systems (TOIS)
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An Information Retrieval (IR) system ranks documents according to their predicted relevance to a formulated query. The prediction depends on the ranking algorithm adopted and on the assumptions about relevance underlying the algorithm. The main assumption is that there is one user, one information need for each query, one location where the user is, and no temporal dimension. But this assumption is unlikely: relevance is context-dependent. Exploiting the context in a way that does not require an high user effort may be effective in IR as suggested for example by Implicit Relevance Feedback techniques. The high number of factors to be considered by these techniques suggests the adoption of a theoretical framework which naturally incorporates multiple sources of evidence. Moreover, the information provided by the context might be a useful source of evidence in order to personalize the results returned to the user. Indeed, the information need arises and evolves in the present and past context of the user. Since the context changes in time, modeling the way in which the context evolves might contribute to achieve personalization. Starting from some recent reconsiderations of the geometry underlying IR and their contribution to modeling context, in this paper some issues which will be the starting point for my PhD research activity are discussed.