通信学报 ›› 2020, Vol. 41 ›› Issue (3): 197-206.doi: 10.11959/j.issn.1000-436x.2020053

• 学术通信 • 上一篇    

位置社交网络中谱嵌入增强的兴趣点推荐算法

刘真,王娜娜,王晓东,孙永奇   

  1. 北京交通大学计算机与信息技术学院,北京 100044
  • 修回日期:2020-02-26 出版日期:2020-03-25 发布日期:2020-03-31
  • 作者简介:刘真(1977- ),女,江西南昌人,博士,北京交通大学副教授,主要研究方向为社会网络计算、推荐系统、大数据分析与挖掘|王娜娜(1995- ),女,山东潍坊人,北京交通大学硕士生,主要研究方向为推荐系统、基于位置的社交网络|王晓东(1995- ),男,湖北黄冈人,北京交通大学硕士生,主要研究方向为社交网络与社会计算|孙永奇(1969- ),男,河南洛阳人,博士,北京交通大学教授,主要研究方向为大数据分析与挖掘、高性能计算
  • 基金资助:
    国家重点研发计划基金资助项目(2019YFB2102501)

Spectral clustering and embedding-enhanced POI recommendation in location-based social network

Zhen LIU,Na’na WANG,Xiaodong WANG,Yongqi SUN   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China
  • Revised:2020-02-26 Online:2020-03-25 Published:2020-03-31
  • Supported by:
    The National Key Research and Development Program of China(2019YFB2102501)

摘要:

为了有效地捕捉LBSN中丰富的签到、社交等多维上下文信息的空间特性,并深层挖掘用户和POI之间的非线性交互,提出了一种谱嵌入增强的POI推荐算法——PSC-SMLP,设计了偏好增强的谱聚类算法PSC和谱嵌入增强的神经网络SMLP。在2个经典数据集上与现有的POI推荐算法相比,PSC-SMLP可以深层学习用户对POI的个性化偏好,在准确率、召回率、nDCG、平均精度等指标中均获得较大提升。

关键词: 基于位置的社交网络, POI推荐, 谱聚类, 谱嵌入, 神经网络

Abstract:

In order to effectively capture the spatial characteristics of multi-dimensional context information in LBSN,and deeply explore the non-linear interaction between users and POIs,a spectral embedding enhanced POI recommendation algorithm,namely PSC-SMLP,was proposed.A preference enhanced spectral clustering algorithm (PSC) and a novel spectral embedded enhanced neural network (SMLP) was designed to solve the above problems.Compared with state-of-the-art algorithms on two datasets,PSC-SMLP has better performance in terms of the precision,recall,nDCG and mean average precision.

Key words: location-based social network, POI recommendation, spectral clustering, spectral embedding, neural network

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