A higher-performance big data-based movie recommendation system

• Published in: Nonlinear engineering Vol. 14; no. 1; pp. 102760 - 22
• Main Authors: Zhu, Lin, Zhuang, Li
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 04.06.2025; Walter de Gruyter GmbH
• Subjects: Big Data; Data base management systems; movie recommendation system; recommendation; Recommender systems; SpringBoot framework
• ISSN: 2192-8029; 2192-8010; 2192-8029
• DOI: 10.1515/nleng-2025-0092

This paper proposes a Movie Recommendation System (MRS) based on big data. MRS adopts B/S architecture and uses front-end and back-end separation modes. The front-end uses the Vue progressive framework, and the back-end uses the SpringBoot back-end development framework combined with MySQL database development. First, the information about the film is crawled from the network by Python crawler technology. Second, the actual needs of the relevant movie management and recommendation system are provided, using SpringBoot, Vue, MySQL, and other technologies equipped with popular movie management, review management, user management, recharge management, and other functions. In addition, this MRS provides a personalized service, recommending films of interest to users based on their preferences. The relevant tests show that MRS has reliable technical support in the later promotion and operation, providing users with a good experience and also laying a solid foundation for system upgrading.