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  

Personalized Recommendation of Social Network Users’Interest Points Based on Probability Matrix Decomposition Algorithm

作　　者：张敏军[1] 华庆一[1] ZHANG Min-jun;HUA Qing-yi(School of Information Science and Technology,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学信息科学与技术学院,西安710127

出　　处：《计算机科学》2020年第12期144-148,共5页Computer Science

基　　金：国家自然科学基金资助项目(61272286);高等学校博士学科点专项科研基金资助项目(20126101110006)。

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。In the social network environment,the traditional personalized recommendation method of social network users’in-terest points has the problems of low prediction accuracy of network users’interest behavior and low coverage of users’social data,which can not fully mine the temporal and spatial sequence characteristics of users’interest points.Therefore,a personalized recommendation method of social network users’interest points based on probability matrix decomposition algorithm is proposed.In the model training pseudo-code group,the numerical results related to the matrix probability mutation operator are calculated to achieve the physical segmentation of the social network,and the node modeling of the social network based on the probability matrix decomposition algorithm is completed.On this basis,the framework of personalized social network is built,and the results are mined according to the characteristics of users’interest behaviors,and the personalized users are selected to recommend nodes,so as to complete the establishment of personalized recommendation method for users’interest points in social network.The practical test results show that,compared with the traditional method,the new personalized recommendation method can predict the interest behavior of network users with the highest accuracy of 100%,and the coverage rate of social data of users is about 75%,which improve the prediction accuracy of interest behavior of network users and the coverage rate of social data of users,and fully excavate the temporal and spatial sequence characteristics of interest points of social network users.

关 键 词：概率矩阵 分结算法 社交网络用户 兴趣点推荐 伪代码群 变异算子 行为特征 时空序列 

分 类 号：TP369[自动化与计算机技术—计算机系统结构]