基于图嵌入的用户加权Slope One算法

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。

User-weighted Slope One Algorithm Based on Graph Embedding

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.