Crowdsourced data stream mining for tourism recommendation

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.