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DEvIR: Data Collection and Analysis for the Recommendation of Events and Itineraries

Diana Nurbakova 1 Léa Laporte 1 Sylvie Calabretto 1 Jérôme Gensel 2, 3
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 : Distributed events such as multi-day festivals and conventions attract thousands of attendees. Their programs are usually very dense, which makes it difficult for users to select activities to perform. Recent works have proposed event and itinerary recommendation algorithms to solve this problem. Although several datasets have been made available for the evaluation of event recommendation algorithms, they do not suit well for the case of distributed events or itinerary recommendation. Based on the study of available online resources, we define dataset attributes required to perform event and itinerary recommendations in the context of distributed events, and discuss the compliance of existing datasets to these requirements. Revealing the lack of publicly available datasets with desired features, we describe a data collection process to acquire the publicly available data from a major comic book convention website. We present the characteristics of the collected data and discuss its usability for evaluating recommendation algorithms.
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Submitted on : Tuesday, November 27, 2018 - 4:06:54 PM
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  • HAL Id : hal-01936794, version 1

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Diana Nurbakova, Léa Laporte, Sylvie Calabretto, Jérôme Gensel. DEvIR: Data Collection and Analysis for the Recommendation of Events and Itineraries. Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS) 2019, Jan 2019, Maui, Hawaii, United States. ⟨hal-01936794⟩

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