计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 144-148.doi: 10.11896/jsjkx.191000064
张敏军, 华庆一
ZHANG Min-jun, HUA Qing-yi
摘要： 在社交网络环境中传统社交网络用户兴趣点的个性化推荐方法存在网络用户兴趣行为的预测精准性低、用户社交数据覆盖率低的问题不能充分挖掘用户兴趣点的时空序列特征为此提出了一种基于概率矩阵分解算法的社交网络用户兴趣点个性化推荐方法.在模型训练的伪代码群中计算与矩阵概率的变异算子相关的数值结果实现社交关系网络的物理分割完成基于概率矩阵分解算法的社交网络节点建模.在此基础上搭建个性化社交网络框架按照用户兴趣行为的特征挖掘结果选择个性化的用户来推荐节点完成社交网络用户兴趣点个性化推荐方法的建立.实用性检测结果表明与传统方法相比应用新型个性化推荐方法后网络用户兴趣行为的预测精准度最高可达100%用户社交数据覆盖率约为75%提高了网络用户兴趣行为的预测精准性和用户社交数据覆盖率社交网络用户兴趣点的时空序列特征得到了充分挖掘.
中图分类号:
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