摘要
以自行研发的福州市旅游出行网站为基础,设计并实现一种基于词频统计的旅游资讯推荐方法.基于node环境,在几大主流旅游网站上抓取当前热门出行资讯文章,利用盘古分词技术对文章进行分词并产生分词库,最后基于TF_IDF算法进行分词库的词频统计,并根据统计结果进行福州市旅游出行网站的资讯推荐.研究结果表明该方法具有运算速度快,推荐准确性高的优点,改善了用户体验.
Based on the self-developed Fuzhou travel website,a recommendation method based on word frequency statistics was designed and implemented.Based on the node environment,popular travel information articles were grabbed from several mainstream travel websites,and the Pangu word segmentation technology was used to divide the articles and generate the word segmentation database.Finally,TF_IDF algorithm was used for word frequency statistics of word segmentation database,and the information recommendation of Fuzhou travel website was carried out according to the statistical results.The results show that the proposed method has the advantages of fast computing speed,high recommendation accuracy and improved user experience.
作者
俞颖
林振通
林燕玲
邵志荣
Yu Ying;Lin Zhentong;Lin Yanling;Shao Zhirong(Yango University;Spatial Data Mining and Application Research Center of Fujian Province)
出处
《哈尔滨师范大学自然科学学报》
CAS
2020年第2期55-59,共5页
Natural Science Journal of Harbin Normal University
基金
福建省教育厅中青年教师科研项目(JAT190977)
福建省大学生创新创业训练计划(S201913468010)
福建省自然科学基金项目(2019J01088)。
关键词
词频统计
资讯推荐
TF_IDF算法
Word frequency statistics
Information recommendation
TF_IDF algorithm
