基于FP-Growth的图上随机游走推荐方法

推荐是促进诸如社交网络等应用活跃度的重要模式,但 庞大 的节点规模以及复杂的节点间关系给社交网络的推荐问题带来了挑战。随机游走是一种能够有效解决这类推荐问题的策略,但传统的随机游走算法没有充分考虑相邻节点间影响力的差异。提出一种基于FP-Growth的图上随机游走推荐方法,其基于社交网络的图结构,引入FP-Growth算法来挖掘相邻节点之间的频繁度,在此基础上构造转移概率矩阵来进行随机游走计算,最后得到好友重要程度排名并做出推荐。该方法既保留了随机游走方法能有效缓解数据稀疏性等特性,又权衡了不同节点连接关系的差异性。实验结果表明,提出的方法比传统随机游走算法的推荐性能更佳。

Recommendation Method Based on Random Walk on Graph Integrated with FP-Growth

Recommendation is one kind of important strategy to promote the active degree of different social networks.However,it is a big challenge to improve the recommendation performance on social networks for the large scale of nodes as well as the complex relationship.Random walk is an effective method to solve such kind of problem,but the traditional random walk algorithm fails to consider the influence of the neighboring nodes adequately.A recommendation method based on random walk on the graph integrated with FP-Growth was proposed,which is based on the graph structure of the social networks.It introduces the FP-Growth algorithm to mine the frequent degree between the adjacent nodes,and then constructs transition probability matrix for random walk computing.Recommendations will be made according to the importance rank of friends.This method not only retains the characteristics of random walk method,such as alleviating the data sparsity effectively,but also weighs the difference of the relationship between diffe-rent nodes.The experimental results show that the proposed method is superior to the traditional random walk algorithm in the recommendation performance.