融合加权异构信息网络的矩阵分解推荐算法  

Matrix Factorization Algorithm with Weighted Heterogeneous Information Network

作　　者：王根生 潘方正[1] Wang Gensheng;Pan Fangzheng(School of Humanities,Jiangxi Univers让y of Finance and Economics,Nanchang 330013,China;School of International Trade and Economics,Jiangxi University of Finance and Economics,Nanchang 330013,China)

机构地区：[1]江西财经大学人文学院,南昌330013 [2]江西财经大学国际经贸学院,南昌330013

出　　处：《数据分析与知识发现》2020年第12期76-84,共9页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目“社交媒体健康信息可信度评估及偏好推荐研究”(项目编号:72061015)、国家自然科学基金项目“自媒体环境下医患关系突发事件网络舆情演化与危机预警研究”(项目编号:71461012);江西省高校人文社会科学项目“融媒体环境下网络社会舆论生态系统的空间特征及管理策略研究”(项目编号:GL19110)的研究成果之一。

摘　　要：【目的】在矩阵分解推荐算法中融入加权异构信息网络中的知识,提高推荐质量。【方法】构造推荐主体的异构信息网络,通过改进的tanh函数计算相关连接权重;在网络中挑选相关元路径,通过信息增益计算不同元路径的权重;计算不同元路径下的用户兴趣相似度,再结合元路径权重得出用户相似度矩阵;将用户相似度矩阵融合到矩阵分解推荐算法的目标函数中。【结果】在Hetrec2011-MovieLens-2k数据集上的实验结果表明,本文算法相比于传统矩阵分解推荐算法(FunkSVD)在准确率、召回率、覆盖率指标上分别提高了4.4%、5.4%、4.6%,均方根误差下降了0.06。【局限】面对海量数据,矩阵分解的效率低下,并且没有考虑用户兴趣漂移问题。【结论】本文算法提高了矩阵分解推荐算法的推荐质量。[Objective] This paper integrates the knowledge of weighted heterogeneous information network to the matrix decomposition algorithm, aiming to improve the quality of recommendation. [Methods] First, we constructed a heterogeneous information network, and calculated the weight of connection with the improved tanh function. Then, we chose the meta paths from the network and computed their weights based on information gains. Third, we decided the similarity of user interests to create a matrix, and integrated the matrix with our algorithm. [Results] We examined the proposed algorithm with the Hetrec2011-MovieLens-2k dataset. Compared with the traditional FunkSVD algorithm, the precision, recall and coverage of our algorithm increased by 4.4%,5.4%, and 4.6%, while its root mean square error reduced by 0.06. [Limitations] The matrix decomposition algorithm could not process massive data efficiently, and we did not investigate the drifting issues of user interests. [Conclusions] The proposed algorithm could effectively generate recommendation results.

关 键 词：推荐算法 矩阵分解 异构信息网络 元路径 信息增益 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] G250[自动化与计算机技术—计算机科学与技术]