基于Python的纵横中文网小说推荐系统研究：利用推荐模型分析销量影响因素。

Based on the content of the document "基于python中文网小说推荐系统论文.doc", the graduation thesis titled "基于python的纵横中文网小说推荐系统" explores the development of a novel recommendation system for Chinese online novels using the Python programming language. The thesis emphasizes the importance of online reading in today's society and how individuals often turn to the internet to search for and read their favorite novels. The thesis highlights the significance of utilizing user identification codes, recognition codes, and sales data to analyze user behavior and reading patterns. By employing network models and training experimental data, the system aims to explore various factors influencing sales and provide a detailed analysis of these factors. Through the use of novel recommendation models, the system can recommend novels based on user preferences and test data analysis. Through the implementation of big data technology, the thesis showcases the ability to crawl Chinese novel websites, gather information about preferred novels, and generate relevant recommendations. The thesis underscores the importance of novel recommendation systems in enhancing the online reading experience and providing personalized recommendations to users. Overall, the graduation thesis demonstrates a strong understanding of recommendation models, Python programming, and the importance of leveraging data to improve user experience in the online reading community. It provides valuable insights into the development of a robust novel recommendation system tailored to the preferences of Chinese online novel readers.