Crowdsourced Data Stream Mining for Tourism Recommendation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Leal, F (Autor) Outra; Veloso, B (Autor) FEP; Malheiro, B (Autor) Outra; Burguillo, JC (Autor) Outra

Ata de Conferência Internacional

Título: Trends and Applications in Information Systems and Technologies - Volume 1, WorldCIST 2021, Terceira Island, Azores, Portugal, 30 March - 2 April, 2021.

Páginas: 260-269

World Conference on Information Systems and Technologies, WorldCIST 2021

1 April 2021 through 2 April 2021

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00T-T3C

DOI: 10.1007/978-3-030-72657-7_25

Abstract (EN): 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. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica