Crowdsourced Data Stream Mining for Tourism Recommendation

Autor:	Benedita Malheiro, Bruno Veloso, Juan C. Burguillo, Fátima Leal
Rok vydání:	2021
Předmět:	High rate Data stream mining Computer science business.industry Profiling Robust statistics Research opportunities Recommendation Crowdsourcing Data science Field (computer science) Tourism Profiling (information science) Crowdsourced data streams business
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030726560 WorldCIST (1)
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8023c324d5a86628145a79eced60aae7 https://doi.org/10.1007/978-3-030-72657-7_25 Zobrazit plný text záznamu