Archivio Istituzionale della RicercaContext-aware personalization is one of the possible ways to face the problem of information overload, that is, the difficulty of understanding an issue and making decisions when receiving too much information. Context-aware personalization can reduce the information noise, by proposing to the users only the information which is relevant to their current contexts. In this work we propose an approach that uses data mining algorithms to automatically infer the subset of data that, for each context, must be presented to the user, thus reducing the information noise.
Reducing big data by means of context-aware tailoring / Garza, Paolo; Quintarelli, Elisa; Rabosio, Emanuele; Tanca, Letizia. - STAMPA. - 637:(2016), pp. 115-127. (Intervento presentato al convegno 20th East-European Conference on Advances in Databases and Information Systems, ADBIS 2016 and Workshop on 3rd International Workshop on Big Data Applications and Principles, BigDap 2016, 2nd Workshop on Data-Centered Smart Applications, DCSA 2016, Workshop on Doctoral Consortium, DC 2016 tenutosi a Prague nel August 28-31, 2016) [10.1007/978-3-319-44066-8_13].
Reducing big data by means of context-aware tailoring
GARZA, PAOLO;
2016
Abstract
Context-aware personalization is one of the possible ways to face the problem of information overload, that is, the difficulty of understanding an issue and making decisions when receiving too much information. Context-aware personalization can reduce the information noise, by proposing to the users only the information which is relevant to their current contexts. In this work we propose an approach that uses data mining algorithms to automatically infer the subset of data that, for each context, must be presented to the user, thus reducing the information noise.
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https://hdl.handle.net/11583/2653424
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