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

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dc.contributor.authorNurbakova, Diana
dc.contributor.authorLaporte, Léa
dc.contributor.authorCalabretto, Sylvie
dc.contributor.authorGensel, Jerome
dc.date.accessioned2019-01-03T00:02:36Z
dc.date.available2019-01-03T00:02:36Z
dc.date.issued2019-01-08
dc.description.abstractDistributed 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.277
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59668
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectDigital and Social Media
dc.subjectdata collection, dataset, distributed events, event recommendation, itinerary recommendation
dc.titleDEvIR: Data Collection and Analysis for the Recommendation of Events and Itineraries
dc.typeConference Paper
dc.type.dcmiText

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