计算机工程 ›› 2018, Vol. 44 ›› Issue (9): 59-63,69.doi: 10.19678/j.issn.1000-3428.0047866

• 先进计算与数据处理 • 上一篇    下一篇

混合时空和流行度特征的兴趣点推荐算法

吴燕,章韵,陈双双   

  1. 南京邮电大学 计算机学院,南京 210003
  • 收稿日期:2017-07-07 出版日期:2018-09-15 发布日期:2018-09-15
  • 作者简介:吴燕(1992—),女,硕士研究生,主研方向为数据挖掘、移动推荐、实体识别;章韵,教授、博士;陈双双,硕士研究生。

Point of Interest Recommendation Algorithm Fusing with Spatiotemporal and Popularity Features

WU Yan,ZHANG Yun,CHEN Shuangshuang   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Received:2017-07-07 Online:2018-09-15 Published:2018-09-15

摘要:

兴趣点推荐有助于用户发现所需位置,但现有推荐算法的精确率较低。为此,提出一种融合时空与流行度特征的个性化兴趣点推荐算法。在基于用户的协同过滤算法中融入时间特征,将基于时间因素的兴趣点流行度估算与空间特征相结合,分别给出相应的估算方法并进行线 性组合,从而得到基于联合框架的兴趣点推荐算法。实验结果表明,相比U、UTF、U+SB算法,该推荐算法能够有效提升推荐精确率和召回率。

关键词: 兴趣点推荐, 协同过滤, 时间特征, 空间特征, 流行度

Abstract:

Point of Interest(POI) recommendation helps users to find the desired location,but the recommendation accuracy of existing recommendation algorithms is low.To solve this problem,a POI recommendation algorithm fusing with spatiotemporal and popularity features is proposed.The time characteristics are integrated into the user based collaborative filtering algorithm,and the estimation of the POI popularity based on time factors is integrated into the spatial characteristics,the corresponding estimation methods are given respectively for the two methods,and a joint framework based POI recommendation algorithm is obtained.Experimental results show that,compared with U,UTF and U+SB algorithm,the proposed recommendation algorithm can effectively improve the recommendation accuracy and recall rate.

Key words: Point of Interest(POI) recommendation, collaborative filtering, time characteristics, spatial characteristics, popularity

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