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A Probabilistic Model for Intrusive Recommendation Assessment

Akermi, Imen and Boughanem, Mohand and Faiz, Rim A Probabilistic Model for Intrusive Recommendation Assessment. (2018) In: 12th ACM conference series on Recommender Systems (RECSYS 2018), 2 October 2018 - 7 October 2018 (Vancouver, Canada).

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Official URL: https://doi.org/10.1145/3240323.3240403

Abstract

The overwhelming advances in mobile technologies allow recommender systems to be highly contextualized and able to deliver recommendation without an explicit request. However, it is no longer enough for a recommender system to determine what to recommend according to the users' needs, but it also has to deal with the risk of disturbing the user during recommendation. We believe that mobile technologies along with contextual information may help alleviate this issue. In this paper, we address intrusiveness as a probabilistic approach that makes use of the several embedded applications within the user's device and the user's contextual information in order to figure out intrusive recommendations that are subject to rejection. The experiments that we conducted have shown that the proposed approach yields promising results.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to ACM. The definitive version is available at http://dl.acm.org This papers appears in RecSys '18 ISBN: 978-1-4503-5901-6 The original PDF is available at: https://dl.acm.org/citation.cfm?id=3240403
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Carthage (TUNISIA)
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Deposited On:23 Jul 2019 10:09

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