
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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Type
Communication / ConférenceDate
2018Conference title
ADMA 2018: International Conference on Advanced Data Mining and ApplicationsConference date
2018-11Conference city
NanjingConference country
ChinaBook title
Advanced Data Mining and Applications 14th International Conference, ADMA 2018Book author
Gan, Guojun; Li, Bohan; Li, Xue; Wang, ShuliangPublisher
Springer
Published in
Cham
ISBN
978-3-030-05089-4
Number of pages
532Pages
381-391
Publication identifier
Metadata
Show full item recordAuthor(s)
Vahidi Ferdousi, ZahraLaboratoire 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 systemRelated items
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