Abstract
Recently, with the development of social and economic levels and people's pursuit of quality of life, tourism has become the first choice for more and more people. However, the traditional travel agency-based tourism method has also begun to expose some problems. However, with the development of machine learning and big data technology, personalized recommendation systems have become more and more important, which also provides us with new ideas to solve this problem. This paper therefore explores the application of spatial clustering algorithm and collaborative filtering algorithm in tourist attraction recommendation. This algorithm can not only mine the geographical location information of tourist attractions, but also analyze and study the data connections between users and attractions, and use these data to make personalized recommendations, providing more considerate and convenient services for travel planning.
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Acknowledgement
This work was supported by the Natural Science Foundation of Shandong Province, China (No. ZR2020QF038), the Ability Improvement Project of Science and Technology SMES in Shandong Province (No. 2023TSGC0279), the Youth Innovation Team of Colleges and Universities in Shandong Province (2023KJ329), and the Qilu University of Technology (Shandong Academy of Sciences) Talent Scientific Research Project (No. 2023RCKY128).
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Jiang, Y., Zhang, Y., Li, Z., Yu, W., Wei, H., Yuan, L. (2024). Tourist Attraction Recommendation System Based on Django and Collaborative Filtering. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2015. Springer, Singapore. https://doi.org/10.1007/978-981-97-0827-7_20
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DOI: https://doi.org/10.1007/978-981-97-0827-7_20
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