A Hybrid Deep Learning-Based Tourism Recommendation System for Personalized Travel Planning

The present paper discusses a mixed approach to the im-plementation of deep learning-based tourism recommenda-tion systems that aims to overcome some of the challenges associated with conventional approaches to building recom-mendation engines. Through an effective combination of ma-chine learning algorithms along with some sophisticated deep learning-based models such as BERT, LSTM, and CNN, the system successfully enhances the accuracy and personalization of recommendations. Thanks to TF-IDF and cosine similarity, efficient processing is provided, and semantic understanding improves the effectiveness of recommendations as well. Thus, the present system successfully resolves the problem of data overload within tourism. In addition to this, the use of context-aware filtering based on various factors, including budget, weather, or seasons, significantly boosts the usefulness of recommendations pro-vided by the model. The utilization of Explainable Artificial Intelligence enables users to better understand the underlying reasoning of destination selection and thus fosters greater trust among travelers. It can be concluded that the developed model possesses high efficiency in terms of accuracy, speed, and convenience. Possible enhancements in future research include the imple-mentation of fully deep learning based systems, the inclusion of cloud technology, and the introduction of personalized recommendations via the study of user’s behavioral patterns.

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