摘要
针对兴趣点推荐系统存在的隐式反馈建模用户-POI交互准确率不高和忽视用户签到数据的隐性反馈属性的问题。提出了一种新颖的兴趣点推荐算法。具体而言,采用一种基于神经网络的排序算法来捕获用户-兴趣点的交互关系,结合泊松分解算法和贝叶斯个性化排序技术建模用户的签到行为,将上述2个步骤得到的算法整合到统一的推荐算法架构中,从而提供兴趣点推荐服务。实验结果表明,提出的算法推荐性能优于传统主流先进兴趣点推荐算法。
The implicit feedback modeling user-Point-of-Interest(POI)interaction accuracy of the POI recommendation system is not high and the implicit feedback attribute of the use’s check-in data is ignored.A novel POI recommendation algorithm is proposed.Specifically,first of all,a neural network-based ranking algorithm is used to capture the interaction relationship of user-POI.Then,the Poisson factorization algorithm and Bayesian personalized ranking technology are combined to model the user’s check-in behavior.The algorithms obtained in the above two steps are integrated into a unified recommendation algorithm architecture to provide a POI recommendation service.The experimental results show that the proposed algorithm is better than the traditional state-of-the-art POI recommendation algorithm.
作者
张松慧
熊汉江
ZHANG Songhui;XIONG Hanjiang(School of Computer,Wuhan Vocational College of Software and Engineering,Wuhan 430205,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第21期176-186,共11页
Computer Engineering and Applications
基金
教育部职业院校信息化教学指导委员会课题(No.2018LXB0286)。
关键词
兴趣点推荐
泊松分解
神经网络
贝叶斯个性化排序
Point-of-Interest(POI)recommendation
Poisson factorization
neural network
Bayesian personalized ranking
作者简介
张松慧(1980—),女,硕士,副教授,CCF会员,研究领域为人工智能、数据挖掘,E-mail:86919931@qq.com;熊汉江(1974—),男,博士,教授,研究领域为测绘遥感、计算机应用技术。
