位置社交网络的潜在好友推荐模型研究

孙晓晨; 徐雅斌

doi:10.3969/j.issn.1000-0801.2014.10.012

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位置社交网络的潜在好友推荐模型研究

研究与开发 | 更新时间：2024-06-05

- 位置社交网络的潜在好友推荐模型研究
- A Potential Friends Recommendation Model for Location-Based Social Network
- 电信科学 2014年30卷第10期页码：71-77
- 作者机构：
  
  1. 北京信息科技大学计算机学院北京 100101
  2. 网络文化与数字传播北京市重点实验室北京 100101
- 作者简介：
  
  [ "孙晓晨，女，北京信息科技大学教授，主要研究方向为社交网络、数据挖掘。" ]
  [ "徐雅斌，男，北京信息科技大学教授，主要研究方向为社交网络、云计算。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61370139);网络文化与数字传播北京市重点实验室基金资助项目;Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(ICDD201309);北京市属高等学校创新团队建设与教师职业发展计划基金资助项目;The Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality(IDHT20130519)
- DOI：10.3969/j.issn.1000-0801.2014.10.012
  中图分类号：
- 网络出版日期：2014-10，
  
  纸质出版日期：2014-10-20
- 稿件说明：
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孙晓晨, 徐雅斌. 位置社交网络的潜在好友推荐模型研究[J]. 电信科学, 2014,30(10):71-77.

Xiaochen Sun, Yabin Xu. A Potential Friends Recommendation Model for Location-Based Social Network[J]. Telecommunications science, 2014, 30(10): 71-77.
孙晓晨, 徐雅斌. 位置社交网络的潜在好友推荐模型研究[J]. 电信科学, 2014,30(10):71-77. DOI： 10.3969/j.issn.1000-0801.2014.10.012.

Xiaochen Sun, Yabin Xu. A Potential Friends Recommendation Model for Location-Based Social Network[J]. Telecommunications science, 2014, 30(10): 71-77. DOI： 10.3969/j.issn.1000-0801.2014.10.012.

摘要

为了提高位置社交网络的服务便捷性和用户感受度，与位置相关的推荐服务越来越具有重要意义和应用需求。提出的潜在好友推荐模型主要是根据签到位置的相似度及好友相似度进行潜在用户推荐。通过用户的好友关系、签到特性及签到历史记录，计算用户在各个位置兴趣点的位置权重，再分别利用位置权重及好友关系计算用户的位置相似度和好友相似度，最后根据用户位置和好友关系的综合相似度进行潜在用户推荐。实验结果表明，提出的潜在好友推荐模型是切实有效的。

Abstract

To make service convenient and improve user experience degrees

recommendation service has become more and more important to users in the location-based social network（LBSN）. An improved potential friends recommendation in LBSN

which is based on the relationship among friends and the similarity of locations

was proposed. First of all

the user interest pointsin each position weight with friends' relationship

check-in feature and historical recordwere calculated. Then position weight and the friend relationship were used to calculate the friends' similarity and location similarity. Finally

potential friends to user based on the friends' similarity and location similarity were recommended. The experimental results show that the proposed model is effective.

关键词

Keywords

references

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