基于Spring Boot和用户画像的组合推荐系统的设计与实现
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摘要
本系统以新闻为例,使用Python进行推荐算法的编写,使用Java实现了Web平台下基于MVC模式的SpringBoot框架。首先仿照Movielens数据集的格式,对新浪网不同类别的新闻数据进行爬取,模拟生成了用户和评分数据存储到MySQL数据库中。随后对评分数据使用了基于用户(内容)的协同过滤算法、SVD模型和隐语义模型;使用jieba分词工具对新闻进行分词,获取到了每条新闻的关键词,根据用户对新闻的浏览历史,得到了一系列关键词的集合,从而生成了用户画像词云;同时可以根据每篇新闻的关键词在数据库中进行通配符查找,获取到了与该新闻相似的新闻。最后根据所建立的组合推荐系统,可以获取到为指定用户的个性化新闻推荐以及与所阅读新闻相似的新闻,从而实现新闻的推荐。
This system takes news as an example, uses Python to write the recommendation algorithm, and uses Java to implement the Spring Boot framework based on MVC mode under the Web platform. Firstly, according to the format of the Movielens dataset, the different types of news data of Sina.com are crawled. The simulated user data and the score data are stored in the MySQL database. Subsequently, the user(content) based collaborative filtering algorithm, SVD model and implicit semantic model are used for the scoring data; the jieba word segmentation tool is used to segment the news, and the keywords of each news are obtained, according to the user's browsing history of the news. A collection of keywords is generated to generate a user portrait cloud; at the same time, a wildcard search can be performed in the database according to the keywords of each news, and news similar to the news is obtained. Finally, according to the established combination recommendation system, the personalized news recommendation for the designated user and the news similar to the read news can be obtained, thereby realizing the recommendation of the news.
This system takes news as an example, uses Python to write the recommendation algorithm, and uses Java to implement the Spring Boot framework based on MVC mode under the Web platform. Firstly, according to the format of the Movielens dataset, the different types of news data of Sina.com are crawled. The simulated user data and the score data are stored in the MySQL database. Subsequently, the user(content) based collaborative filtering algorithm, SVD model and implicit semantic model are used for the scoring data; the jieba word segmentation tool is used to segment the news, and the keywords of each news are obtained, according to the user's browsing history of the news. A collection of keywords is generated to generate a user portrait cloud; at the same time, a wildcard search can be performed in the database according to the keywords of each news, and news similar to the news is obtained. Finally, according to the established combination recommendation system, the personalized news recommendation for the designated user and the news similar to the read news can be obtained, thereby realizing the recommendation of the news.
引文
[1]王家南.基于文本标签的推荐算法研究[D].郑州:郑州大学,2018.WANG Jia-nan.Research on personalized recommendation algorithm based on text label[D].Zhengzhou:Zhengzhou University,2018.
[2]毕闰芳.基于SVR的协同过滤与用户画像融合的电影个性化推荐研究[D].郑州:郑州大学,2018.BI Yi-fang.Research on film personalized recommendation based on SVR collaborative filtering and user portrait fusion[D].Zhengzhou:Zhengzhou University,2018.
[3]方圆.基于Spring Boot的综合数据管理系统的设计与实现[D].大连:大连理工大学,2018.FANG Yuan.Design and implementation of integrated data management system based on Spring Boot[D].Dalian:Dalian University of Technology,2018.
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[8]王明宇,戴蔚伯.大数据时代的计算机信息处理技术分析[J].电子元器件与信息技术,2018,2(02):1-3.WANG Ming-yu,DAI Wei-bo,ZHANG Shi-qi.Analysis of Computer Information Processing Technology in the Age of Big Data[J].Electronic Components&Information Technology,2018,2(02):1-3.
[9]高冉冉.基于用户日志的新闻推荐系统研究[D].长春:吉林大学,2018.GAO Ran-ran.Research on news recommendation system based on user log[D].Changchun:Jilin University,2018.
[10]王珂.实时新闻推荐系统的设计与实现[D].北京:北京交通大学,2017.WANG Wei.Design and implementation of real-time news recommendation system[D].Beijing:Beijing Jiaotong University,2017.
[2]毕闰芳.基于SVR的协同过滤与用户画像融合的电影个性化推荐研究[D].郑州:郑州大学,2018.BI Yi-fang.Research on film personalized recommendation based on SVR collaborative filtering and user portrait fusion[D].Zhengzhou:Zhengzhou University,2018.
[3]方圆.基于Spring Boot的综合数据管理系统的设计与实现[D].大连:大连理工大学,2018.FANG Yuan.Design and implementation of integrated data management system based on Spring Boot[D].Dalian:Dalian University of Technology,2018.
[4]王科强.基于矩阵分解的个性化推荐系统[D].上海:华东师范大学,2017.WANG Ke-qiang.Personalized recommendation system based on matrix decomposition[D].Shanghai:East China Normal University,2017.
[5]王升升,赵海燕,陈庆奎,等.个性化推荐中的隐语义模型[J].小型微型计算机系统,2016,37(05):881-889.WANG Sheng-sheng,ZHAO Hai-yan,CHEN Qing-kui.The Implicit Semantic Model in Personalized Recommendations[J].Mini-micro Systems,2016,37(05):881-889.
[6]夏明星.基于情感分析的评论极性分类和电影推荐系统的设计与实现[D].合肥:安徽大学,2016.XIA Ming-xing.Design and implementation of commentary polarity classification and film recommendation system based on sentiment analysis[D].Hefei:Anhui University,2016.
[7]王晓洁,秦冰峰.基于Spring Boot2.0框架的信息管理系统的设计与实现[J].电子元器件与信息技术,2018(12):29-31.WANG Xiao-jie,QINBing-feng.Design and Implementation of Information Management System Based on Spring Boot2.0 Framework[J].Electronic Components&Information Technology,2018(12):29-31.
[8]王明宇,戴蔚伯.大数据时代的计算机信息处理技术分析[J].电子元器件与信息技术,2018,2(02):1-3.WANG Ming-yu,DAI Wei-bo,ZHANG Shi-qi.Analysis of Computer Information Processing Technology in the Age of Big Data[J].Electronic Components&Information Technology,2018,2(02):1-3.
[9]高冉冉.基于用户日志的新闻推荐系统研究[D].长春:吉林大学,2018.GAO Ran-ran.Research on news recommendation system based on user log[D].Changchun:Jilin University,2018.
[10]王珂.实时新闻推荐系统的设计与实现[D].北京:北京交通大学,2017.WANG Wei.Design and implementation of real-time news recommendation system[D].Beijing:Beijing Jiaotong University,2017.