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ANASTASIA : recommandation de séquences d'activités spatio-temporelles

Diana Nurbakova 1 Léa Laporte 1 Sylvie Calabretto 1 Jérôme Gensel 2
1 DRIM - Distribution, Recherche d'Information et Mobilité
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 STEAMER - Spatio-temporal information systems
LIG - Laboratoire d'Informatique de Grenoble
Abstract : As amount of activities available for users and their variety have grown, personalised recommendation of activities sequences has become an important challenge. However, most of recommender systems do not consider temporal constraints of activities, making the recommendation hard for user to follow. In this article, we describe a novel approach for recommendation of competing activities limited in time. It makes use of historical records of users' activities in order to mine users' behavioral patterns, and combines different contextual elements (popularity , demographic and spatio-temporal information). We present an evaluation framework and a dataset that will allow us to evaluate our approach.
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Submitted on : Tuesday, June 7, 2016 - 3:20:28 PM
Last modification on : Wednesday, November 3, 2021 - 6:47:03 AM


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  • HAL Id : hal-01328062, version 1


Diana Nurbakova, Léa Laporte, Sylvie Calabretto, Jérôme Gensel. ANASTASIA : recommandation de séquences d'activités spatio-temporelles. Rencontres Jeunes Chercheurs en Recherche d'Information (RJCRI CORIA-CIFED), Mar 2016, Toulouse, France. pp.10. ⟨hal-01328062⟩



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