一种基于元路径的社交事件推荐方法

冯堂林; 何亮

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一种基于元路径的社交事件推荐方法

更新时间：2026-09-04

- 一种基于元路径的社交事件推荐方法
- Meta-path based social event recommendation
- 微电子学与计算机 2021年38卷第8期页码：73-79
- 作者机构：
  
  西安交通大学计算机学院,陕西,西安,710049
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 11574120
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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冯堂林, 何亮. 一种基于元路径的社交事件推荐方法[J]. 微电子学与计算机, 2021, 38(8): 73-79.

FENG Tanglin, HE Liang. Meta-path based social event recommendation[J]. 2021, 38(8): 73-79.
冯堂林, 何亮. 一种基于元路径的社交事件推荐方法[J]. 微电子学与计算机, 2021, 38(8): 73-79. DOI：

FENG Tanglin, HE Liang. Meta-path based social event recommendation[J]. 2021, 38(8): 73-79. DOI：

摘要

基于事件的社交网络为用户提供线上线下互动模式的社交服务，用户在线上组织事件，线下参与事件.以往的研究基于因素或基于图实现社交事件推荐，难以充分挖掘社交网络中的语义信息.为解决该问题，提出了基于元路径的社交事件推荐方法，元路径包含丰富的语义信息，是异质信息网络的重要特征.首先构建了一个异质社交网络，接下来利用元路径计算用户与事件之间的相关性矩阵，在此基础上，通过最优化问题求解元路径权重，最后将相关性矩阵和元路径权重线性组合实现面向用户的社交事件推荐.该算法在应用最为广泛的基于事件的社交网络平台--Meetup网站真实数据集上进行了实验，采用不同度量指标与其它推荐方法进行了对比，实验结果表明本算法优于其它对比方法.

Abstract

Event based social networks (EBSNs) provides users with online to offline social services. Users can organize events online and participate in them offline. Particularly

social event recommendation is a hot issue in this field. The existing studies have mainly focused on factors-based or graph-based social event recommendation

which can hardly exploit the semantic information in the social networks thoroughly. To address this problem

a meta-path based social event recommendation method is proposed. The meta-path contains rich semantic information

being an important feature of the heterogeneous information networks. Firstly

a heterogeneous network is constructed

and then various candidate meta-paths are utilized to calculate the correlation matrix between the users and events. On this basis

the weight of each meta-path is solved by optimization method. Accordingly

the social event recommendation is realized in terms of the linear combination of correlation matrix and meta-path weights. The algorithm is validated on the real dataset of Meetup

the most popular event-based social network platform. Meanwhile

various metrics are explored to compare the recommendation methods. The experimental results show that the proposed algorithm outperforms the comparative ones.

关键词

Keywords

references

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