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Correlation-Based Pre-Filtering for Context-Aware Recommendation

Vahidi Ferdousi, Zahra; Colazzo, Dario; Negre, Elsa (2018), Correlation-Based Pre-Filtering for Context-Aware Recommendation, in Roussos, George; Kameas, Achilles, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Institute of Electrical and Electronics Engineers. 10.1109/PERCOMW.2018.8480278

Type
Communication / Conférence
Date
2018
Conference title
2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
Conference date
2018-03
Conference city
Athens
Conference country
Greece
Book title
2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
Book author
Roussos, George; Kameas, Achilles
Publisher
Institute of Electrical and Electronics Engineers
ISBN
978-1-5386-3227-7
Publication identifier
10.1109/PERCOMW.2018.8480278
Metadata
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Author(s)
Vahidi Ferdousi, Zahra
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Colazzo, Dario
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Negre, Elsa
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
With the increasing use of connected devices and IoT, users' contextual information is more and more available and used in different information systems. One of the domains where the use of contextual information is promising is that of recommendation. As a matter of fact, context-aware recommender systems (CARSs) have demonstrated that taking contextual information about users into account can improve the effectiveness of recommendation, by generating more relevant recommendations to the users in their specific contextual situation. In this paper we propose a new context representation and approach to integrate this kind of information into a recommender system. We make a strong representation of the context, based on the influence of context on ratings, calculated using the Pearson Correlation Coefficient. We do a pre-filtering recommendation based on this representation. Our evaluations demonstrate that our approach can outperforms the state of the art.
Subjects / Keywords
Context-Aware Recommender System; Contextual Information Integration; Contextual Pre-Filtering; Collaborative Filtering; Matrix Factorization

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