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

doi:10.11959/j.issn.1000-436x.2020053

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位置社交网络中谱嵌入增强的兴趣点推荐算法

学术通信 | 更新时间：2024-06-05

- 位置社交网络中谱嵌入增强的兴趣点推荐算法
- Spectral clustering and embedding-enhanced POI recommendation in location-based social network
- 通信学报 2020年41卷第3期页码：197-206
- 作者机构：
  
  北京交通大学计算机与信息技术学院，北京 100044
- 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" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2019YFB2102501)
- DOI：10.11959/j.issn.1000-436x.2020053
  中图分类号： TP391
- 网络出版日期：2020-03，
  
  纸质出版日期：2020-03-25
- 稿件说明：
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刘真, 王娜娜, 王晓东, 等. 位置社交网络中谱嵌入增强的兴趣点推荐算法[J]. 通信学报, 2020,41(3):197-206.

Zhen LIU, Na’na WANG, Xiaodong WANG, et al. Spectral clustering and embedding-enhanced POI recommendation in location-based social network[J]. Journal on communications, 2020, 41(3): 197-206.
刘真, 王娜娜, 王晓东, 等. 位置社交网络中谱嵌入增强的兴趣点推荐算法[J]. 通信学报, 2020,41(3):197-206. DOI： 10.11959/j.issn.1000-436x.2020053.

Zhen LIU, Na’na WANG, Xiaodong WANG, et al. Spectral clustering and embedding-enhanced POI recommendation in location-based social network[J]. Journal on communications, 2020, 41(3): 197-206. DOI： 10.11959/j.issn.1000-436x.2020053.

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。

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.

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Keywords

references

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