Erkal, NecatiSaran, Nurdan2025-05-112025-05-112024979835038897897983503889612165-0608https://doi.org/10.1109/SIU61531.2024.10600781https://hdl.handle.net/20.500.12416/9548The importance of recommender systems has increased recently. It's due to the complexity of the data. It is becoming increasingly difficult to make recommendations that users might like. This is especially true in trip recommender systems, where recommending the next city is a challenging task. Deep learning has been shown to improve recommendation accuracy and handle complex data in various studies. This study presents new architectures, data, and hyperparameter tuning techniques for a deep learning-based trip recommender system. The study analyzes the algorithm and dataset of the NVIDIA Team's winning solution in the WSDM WebTour 2021 Challenge and proposes enhancements to it.trinfo:eu-repo/semantics/closedAccessTrip RecommendationWebtour 2021Deep Learning-Based Recommender SystemsConference Object10.1109/SIU61531.2024.106007812-s2.0-85200861998