计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (21): 176-186.DOI: 10.3778/j.issn.1002-8331.1909-0072

• 模式识别与人工智能 • 上一篇    下一篇

融合神经网络和泊松分解的兴趣点推荐算法

张松慧,熊汉江   

  1. 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
  • 出版日期:2020-11-01 发布日期:2020-11-03

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

ZHANG Songhui, XIONG Hanjiang   

  1. 1.School of Computer, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China
    2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Online:2020-11-01 Published:2020-11-03

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。

关键词: 兴趣点推荐, 泊松分解, 神经网络, 贝叶斯个性化排序

Abstract:

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.

Key words: Point-of-Interest(POI) recommendation, Poisson factorization, neural network, Bayesian personalized ranking