Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3502
Title: Crowdsourced data stream mining for tourism recommendation
Authors: Leal, Fátima
Veloso, Bruno
Malheiro, Benedita
Burguillo, Juan C.
Keywords: Crowdsourced data streams
Data stream mining
Profiling
Recommendation
Tourism
Issue Date: Apr-2021
Publisher: Springer
Citation: Leal F., Veloso B., Malheiro B.,& Burguillo J.C. (2021). Crowdsourced Data Stream Mining for Tourism Recommendation. In: Rocha Á., Adeli H., Dzemyda G., Moreira F., & Ramalho Correia A.M. (eds) Trends and Applications in Information Systems and Technologies, WorldCIST 2021. Advances in Intelligent Systems and Computing (1365, pp. 160-169). Doi:10.1007/978-3-030-72657-7_25. Disponível no Repositório UPT, http://hdl.handle.net/11328/3502
Abstract: Crowdsourced data streams are continuous flows of data generated at high rate by users, also known as the crowd. These data streams are popular and extremely valuable in several domains. This is the case of tourism, where crowdsourcing platforms rely on tourist and business inputs to provide tailored recommendations to future tourists in real time. The continuous, open and non-curated nature of the crowd-originated data requires robust data stream mining techniques for on-line profiling, recommendation and evaluation. The sought techniques need, not only, to continuously improve profiles and learn models, but also be transparent, overcome biases, prioritise preferences, and master huge data volumes; all in real time. This article surveys the state-of-art in this field, and identifies future research opportunities.
URI: http://hdl.handle.net/11328/3502
ISBN: 978-3-030-72657-7
Appears in Collections:REMIT - Comunicações a Congressos Internacionais / Papers in International Meetings

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