Exploring models, data strategies, and hyperparameter tuning in a trip recommendation system

Recommender systems' importance has been increasing recently. It is becoming more difficult to make a recommendation that users might like because of the complex data. Especially in trip recommender systems, recommending the next city is a hard task, where recommending truly is important. According to various studies, using deep learning with recommender systems helps improve recommendations' accuracy and handle complex data. This thesis reveals new architectures, data, and hyperparameter tuning techniques for a proposed deep learning-powered trip recommender system. NVIDIA Team's winning recommender system solution in the WSDM WebTour 2021 Challenge has been used. To understand this winning solution, the algorithm and dataset have been analyzed. Then new solutions have been proposed to enhance the study.