基于分词算法的用户个性化推荐系统设计

Design of User Personalized Recommendation System Based on Word Segmentation Algorithm

针对用户浏览的文本内容进行学习,经过中文分词,数据清洗,关键特征提取阶段对文本内容进行深度理解和挖掘,然后在后台数据库中搜索匹配出最贴合该用户兴趣的文本供用户浏览,实现用户个性化内容推荐。

This paper points to text content when user browse,after word segmentation,data cleaning,feature extraction steps,text content is understood and mining deeply.Then this system find the most suitable,interesting content in database for user,so to achieve personalized recommendation.