Recommendation of Tourism Resources Supported by Crowdsourcing

Context-aware recommendation of personalised tourism resources is possible because of personal mobile devices and powerful data filtering algorithms. The devices contribute with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms provide the ability to match the profile (interests and the context) of the tourist against a large knowledge bases of tourism resources. While, in terms of technology, personal mobile devices can gather user-related information, including the user context and access multiple data sources, the creation and maintenance of an updated knowledge base of tourism-related resources requires a collaborative approach due to the heterogeneity, volume and dynamic nature of the resources. The current PhD thesis aims to contribute to the solution of this problem by adopting a Crowdsourcing approach for the collaborative maintenance of the knowledge base of resources, Trust and Reputation for the validation of uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources

A Personalized Travel System Based on Crowdsourcing Model

Lecture Notes in Computer Science, vol. 8933 entitled:Advanced data mining and applications: 10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014: proceedingsWith the proliferation of the online tourism markets, and the rapid change of tourists demands, existing online travel platforms cannot satisfy tourists to some extent, since their tourism demands tend to be more personalized and dynamic. Based on the above motivations, we design and develop a personalized tourism system based on a novel cooperation crowdsourcing model through the Internet. More importantly, data quality control based on the crowdsourcing model is a key problem which affects the accuracy and effectiveness of tourist recommendation. To address this problem, we propose three data quality control schemes for personalized tours based on the crowdsourcing model. Extensive experiments validate the effectiveness of our proposed approach