结合强弱联系和兴趣的社交网络推荐算法

何炜俊; 周应堂

doi:10.12052/gdutxb.180112

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结合强弱联系和兴趣的社交网络推荐算法

综合研究 | 更新时间：2026-05-27

- 结合强弱联系和兴趣的社交网络推荐算法
- A Social Network Recommendation Algorithm Combining Strong and Weak Ties and Interests
- 广东工业大学学报 2019年36卷第3期页码：39-46
- 作者机构：
  
  1. 广东工业大学　管理学院,广东,广州,510520
  2. 南京农业大学　工学院,江苏,南京,210031
- 作者简介：
  
  何炜俊(1995–)，男，硕士研究生，主要研究方向为推荐系统
  周应堂(1963–)，男，副教授，博士，硕士生导师，主要研究方向为管理创新. E-mail：791277872@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(71740024，71472036);江苏高校哲学社会科学重点项目(2017ZDIXM015)
- DOI：10.12052/gdutxb.180112
  中图分类号： C931.6
- 网络首发：2019-05-09，
  
  纸质出版：2019
- 稿件说明：
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何炜俊, 周应堂. 结合强弱联系和兴趣的社交网络推荐算法[J]. 广东工业大学学报, 2019, 36(3): 39-46.

He Wei-jun, Zhou Ying-tang. A Social Network Recommendation Algorithm Combining Strong and Weak Ties and Interests[J]. 2019, 36(3): 39-46.
何炜俊, 周应堂. 结合强弱联系和兴趣的社交网络推荐算法[J]. 广东工业大学学报, 2019, 36(3): 39-46. DOI： 10.12052/gdutxb.180112.

He Wei-jun, Zhou Ying-tang. A Social Network Recommendation Algorithm Combining Strong and Weak Ties and Interests[J]. 2019, 36(3): 39-46. DOI： 10.12052/gdutxb.180112.

摘要

提出了一种结合强弱联系和兴趣的社交网络推荐算法. 首先

考虑依据强弱联系为客户构建社交关系集合

同时兼顾信息传递的广度和深度. 然后

基于关联规则改进传统PageRank算法的状态转移概率

修正的矩阵能够更合理地度量不同客户之间的社交紧密程度. 同时

考虑客户之间的兴趣爱好相似性

赋予其对候选项目投票的权重

旨在提高系统的多样性和新颖性. 最后

综合上述两者对候选项目进行评分并作Top-

过滤得到推荐列表. 实验结果表明

本算法相对于参照算法更具合理性和有效性.

Abstract

Social network recommender systems combining strong and weak ties and interests are proposed. First

social relationship sets for customers are built based on the strong and weak ties

taking into account the breadth and depth of information transfer. Then

based on the association rules to improve the state transition probability of the PageRank Algorithm

the modified matrix is capable of measuring the social closeness between different customers more reasonably. Meanwhile

aiming to increase diversity and novelty of the systems

the similarity of interests between different customers is considered giving them the weight of voting in the candidate projects. Finally

the candidate items are scored and Top-

filtered to obtain a recomm

endation list. As is shown in the experiment results

the algorithm is more reasonable and effective than the reference algorithms.

关键词

Keywords

references

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