"基于协同过滤的旅游推荐系统设计与实现" - 毕业论文专业实践总结

Abstract This resource is a graduation thesis based on the collaborative filtering recommendation algorithm. Collaborative filtering is a commonly used recommendation algorithm that analyzes users' historical behavior and interests to find similar users or items, thereby providing personalized recommendations. This thesis mainly studies the principle of collaborative filtering recommendation algorithm, implementation methods, and the evaluation of its effectiveness in practical applications. Target Audience This resource is suitable for graduate and undergraduate students in the fields of computer science, data science, artificial intelligence, as well as scholars and researchers interested in recommendation algorithms. Use Cases and Objectives This resource can be used for academic research, thesis writing, algorithm implementation, and application scenarios. Through studying this thesis, readers can understand the basic principles and implementation methods of the collaborative filtering recommendation algorithm, and optimize and improve the algorithm in practical applications. The goal is to provide a research framework for collaborative filtering recommendation algorithms, helping readers to deeply understand and apply the algorithm. Further Details The thesis provides detailed algorithm descriptions, experimental design, result analysis, as well as discussions on the advantages and disadvantages of the collaborative filtering algorithm. Readers can refer to this thesis for further research and practical applications based on their own needs and research directions. Keywords: Collaborative filtering, recommendation algorithm, graduation thesis, personalized recommendation, algorithm implementation, effectiveness evaluation.