Towards adaptive and transparent tourism recommendations: A survey

Abstract

Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non‐curated nature of the crowd‐originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state‐of‐art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream‐based scenario, and identifies future research opportunities.