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.
This research is partially supported by the IT2Rail project funded by European Union’s Horizon 2020 research and innovation program under grant agreement No: 636078, and by the grant “IBM – International Business Machine – 2014”.
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Notes
- 1.
Context evolution [10] is the research topic that takes this into account; however, if this task is performed by the designer, it makes his or her burden even heavier.
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Garza, P., Quintarelli, E., Rabosio, E., Tanca, L. (2016). Reducing Big Data by Means of Context-Aware Tailoring. In: Ivanović, M., et al. New Trends in Databases and Information Systems. ADBIS 2016. Communications in Computer and Information Science, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-44066-8_13
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DOI: https://doi.org/10.1007/978-3-319-44066-8_13
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