摘要
随着国民生活水平的提高,旅游业蓬勃发展,旅游业与互联网的结合促进了在线旅游业的形成,也就是当代所说的"智慧旅游"。用户可以通过互联网了解各种各样的旅游信息,但是,日趋严重的过载旅游数据现象让旅游商们难以准确的挖掘出符合用户兴趣的个性化旅游信息,推荐出一个智慧的旅游路线更是如同大海捞针,而旅游推荐系统是解决这一问题的关键技术。本文基于个性化推荐算法的研究,将用户信息,用户评论,用户行为,用户历史订单,用户未来订单等多项数据作为算法的训练测试集,对功能性需求进行分析,开发了基于用户数据的推荐系统。
With the improvement of the living standards of the people and the booming tourism industry,the combination of tourism and the Internet has promoted the formation of online tourism,which is also known as"smart tourism".Users can learn a variety of travel information through the Internet.However,the increasingly serious phenomenon of overloaded travel data makes it difficult for travellers to accurately mine personalized travel information that suits their interests.It is more like recommending a smart travel route.A needle in a haystack,and a travel recommendation system is the key technology to solve this problem.Based on the research of personalized recommendation algorithm,this paper uses user data,user comments,user behavior,user history orders,user future orders and other data as the training test set of the algorithm,analyzes the functional requirements,and studies the system summary design.
作者
周家昊
李民
ZHOU Jia-hao;LI Min(Kunming University of Science and Technology,College of Mechanical and Electrical Engineering,Kunming,Yunnan 650500,China)
出处
《软件》
2019年第11期174-177,共4页
Software
关键词
旅游数据
推荐算法
数据挖掘
Travel data
Recommendation algorithm
Data mining
