计算机与现代化 ›› 2020, Vol. 0 ›› Issue (08): 69-75.doi: 10.3969/j.issn.1006-2475.2020.08.011

• 算法分析与设计 • 上一篇    下一篇

基于图嵌入的用户加权Slope One算法

  

  1. (华南理工大学数学学院,广东广州510640)
  • 收稿日期:2020-01-08 出版日期:2020-08-17 发布日期:2020-08-17
  • 作者简介:钟志松(1994-),男,广东湛江人,硕士研究生,研究方向:大数据,分布式推荐系统,E-mail: zzsscut@163.com; 彭清桦(1999-),男,广东湛江人,本科生,研究方向:数据挖掘,E-mail: pontsh@163.com; 吴广潮(1972-),男,广东潮阳人,副教授,硕士生导师,研究方向:机器学习与数据挖掘,E-mail: magchwu@scut.edu.cn。

User-weighted Slope One Algorithm Based on Graph Embedding

  1. (School of Mathematics, South China University of Technology, Guangzhou 510640, China)
  • Received:2020-01-08 Online:2020-08-17 Published:2020-08-17

摘要: 针对传统Slope One推荐算法在稀疏数据集上预测准确率较低的问题,提出一种基于图嵌入的加权Slope One算法。本文算法首先以融合时间信息的用户相似度为边权建立用户关联图,对该图进行图嵌入得到用户特征向量,然后基于Canopy聚类对用户进行类内加权Slope One推荐。另外,为优化算法性能,本文算法基于Spark计算框架实现。实验结果表明,对比传统的加权Slope One,本文算法在稀疏数据集和显式、隐式评分数据集上的推荐效果和评分预测准确率都更优。

关键词: 图嵌入, 时间信息, Canopy 聚类, 加权Slope One算法, Spark

Abstract: Aiming at the problem of low prediction accuracy of the traditional Slope One recommendation algorithm on sparse data set, this paper proposes a weighted Slope One algorithm based on graph embedding. This algorithm first establishes a correlation graph with time-aware user similarity as the edges’ weight, and obtains user eigen vectors based on the graph embedding of this graph. It then produces intra-class weighted Slope One recommendations using Canopy clustering. Additionally, to optimize the performance of the algorithm, we make an implementation based on the Spark computing framework. Experimental results demonstrate that, compared with the traditional weighted Slope One algorithm, the proposed algorithm has better recommendation effect and score prediction accuracy on both sparse data sets, explicit and implicit scoring data sets.

Key words: graph embedding, time factor, Canopy clustering, weighted Slope One, Spark

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