Tourism recommendation model integrating IGA and multi-source heterogeneous data

Abstract With the improvement of people's living standards, tourism users are paying more and more attention to high-quality and personalized services. However, there are currently problems with tourism recommendation, such as a lack of diversity in tourism planning routes. Therefore, a tourism recommendation model that integrates multiple algorithms and heterogeneous data sources is constructed to address these issues. This model combines interactive genetic algorithm and marine predator algorithm for scenic spot guidance search during construction, and then combines point cloud technology for scenic spot feature extraction. The experimental results show that the hybrid algorithm outperforms the marine predator algorithm, grey wolf optimization algorithm, and deep learning optimization algorithm in terms of recall rate, image recognition accuracy, and route revenue testing, with specific values of 97.88%, 98.12%, and 0.94, respectively. In addition, empirical analysis of the constructed tourist attraction recommendation model found that the normalized cumulative loss gain is generally between 0.8 and 0.9, and the predictions made for the attraction recommendation module also match the actual results, providing users with good experience. The above results indicate that the proposed tourism recommendation model has strong prediction accuracy, and the feature information extraction effect of scenic spots is good, which is conducive to user scenic spot recommendation. This study will help transform the actual needs of future tourism users and further enhance the intelligent tourism service platform.