A Multi-Criteria Recommendation Technique for Personalized Tourism Experiences

Abstract

This research aims to design a multi-Criteria recommendation technique for the tourism domain. In the past few decades, the growth of the World Wide Web has led to an unprecedented amount of information available to us. This phenomenon has resulted in what we now call information overload, where the sheer volume of data surpasses our capacity to manage it effectively. In order to address this issue, it is crucial to ensure that accurate information is communicated to the appropriate audience, as proper information dissemination is key. Like other industries, the tourism industry faces the challenge of information overload. The abundance of information can be overwhelming for both tourists and industry stakeholders. Some tourists like guided tours, while others prefer exploring independently, and that is where e-tour guides can be helpful. E-tour guides are digital tools like apps or websites that inform tourists about their destination. In this context, recommendation systems and information dissemination are closely related. Recommendation systems use algorithms to analyze user data and provide personalized recommendations based on their past behaviour and preferences. Both recommendation systems and information dissemination aim to provide users with relevant and useful information. This study aims to develop a multi-criteria recommendation system that effectively addresses the issue of information overload in the tourism industry by delivering pertinent information to the right users. The proposed method combines several techniques, including deep learning and traditional techniques, such as matrix factorization, to address common challenges like scalability and data sparseness. The approach is designed to provide personalized recommendations based on user location, site, and other relevant criteria. By emphasizing the filtering and provision of the most relevant information based on user location and site, the user experience and engagement can be significantly improved. This study utilizes the Convolutional Matrix Factorization (CMF) algorithm due to its compatibility with tourism. The proposed algorithm, CMF with ResNet, combines the power of CMF with the superior performance of ResNet to overcome the limitations of CMF and achieve even better results for recommendation tasks. Using ResNet, the algorithm can learn more complex and nuanced patterns in the data, leading to more accurate recommendations. In the end, compared to all the tested algorithms, the proposed method outperformed and achieved higher scores on error measurement metrics. Additionally, it can handle complex problems like sparse data or very small amounts of training samples. The proposed method also provided more relevant recommendations to users compared to all other tested algorithms. Overall, the proposed method offers a novel and effective solution to the information overload challenge in the tourism industry