基于PageRank和谱方法的个性化推荐算法

传统的PageRank推荐算法的可扩展性较差。针对这一问题,提出融合PageRank和谱方法的个性化推荐算法。通过在PageRank算法迭代过程中加入候选集节点数来控制迭代的次数,同时利用阈值来修剪参与迭代的节点个数,从而得到候选节点集；采用谱聚类对候选集进行排序,归一化候选节点邻接矩阵,使用矩阵的特征值与特征向量来评估图中节点与目标节点之间的距离,从而产生最终的推荐列表。实验结果表明,所提推荐算法在保证推荐质量的前提下,提高了处理效率。

Personalized Recommendation Algorithm Based on PageRank and Spectral Method

Traditional PageRank recommendation algorithm is less scalable.To solve this problem,a personalized recom-mendation algorithm based on PageRank and spectral method was proposed.The number of iterations is controlled by adding the number of nodes in the PageRank algorithm to obtain the candidate set,threshold is ued to trim the number of nodes participating in the iteration to get the candidate node set.Spectral clustering is utilized to sort the candidate nodes.The candidate node adjacency matrix is normalized,and eigenvalues and eigenvectors of matrices are used to eva-luate the distance between nodes and target nodes in a graph.At last,a final list of recommendations is produced.Experi-mental results show that the proposed recommendation algorithm improves the processing efficiency on the premise of ensuring the recommendation quality.