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CBPF: Leveraging Context and Content Information for Better Recommendations

Vahidi Ferdousi, Zahra; Colazzo, Dario; Negre, Elsa (2018), CBPF: Leveraging Context and Content Information for Better Recommendations, in Gan, Guojun; Li, Bohan; Li, Xue; Wang, Shuliang, Advanced Data Mining and Applications 14th International Conference, ADMA 2018, Springer : Cham, p. 381-391. 10.1007/978-3-030-05090-0_32

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156985097062844.pdf (215.1Kb)
Type
Communication / Conférence
Date
2018
Conference title
ADMA 2018: International Conference on Advanced Data Mining and Applications
Conference date
2018-11
Conference city
Nanjing
Conference country
China
Book title
Advanced Data Mining and Applications 14th International Conference, ADMA 2018
Book author
Gan, Guojun; Li, Bohan; Li, Xue; Wang, Shuliang
Publisher
Springer
Published in
Cham
ISBN
978-3-030-05089-4
Number of pages
532
Pages
381-391
Publication identifier
10.1007/978-3-030-05090-0_32
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)
Recommender systems (RS) help users to find their appropriate items among large volumes of information. Among the different types of RS, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is. In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson Correlation Coefficient. The proposed solution aims at taking advantage of content and contextual information in the recommendation process. We evaluate and show effectiveness of our approach on three different contextual datasets and analyze the performances of the variants of our approach based on the characteristics of these datasets, especially the sparsity level of the input data and amount of available information.
Subjects / Keywords
Context-aware recommender system; Contextual information integration; Pre-filtering recommender system

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    Vahidi Ferdousi, Zahra; Colazzo, Dario; Negre, Elsa (2018) Communication / Conférence
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