融合神经网络和泊松分解的兴趣点推荐算法  

Point-of-Interest Recommendation Algorithm Based on Poisson Factorization and Neural Network

作　　者：张松慧[1] 熊汉江[2] 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)

机构地区：[1]武汉软件工程职业学院计算机学院,武汉430205 [2]武汉大学测绘遥感信息工程国家重点实验室,武汉430079

出　　处：《计算机工程与应用》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)。

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.

关 键 词：兴趣点推荐 泊松分解 神经网络 贝叶斯个性化排序 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]