计算机科学 ›› 2017, Vol. 44 ›› Issue (6): 232-236.doi: 10.11896/j.issn.1002-137X.2017.06.039
卞梦阳,杨青,张敬伟,张会兵,钱俊彦
BIAN Meng-yang, YANG Qing, ZHANG Jing-wei, ZHANG Hui-bing and QIAN Jun-yan
摘要: 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。
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