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数据分析与知识发现  2020, Vol. 4 Issue (12): 76-84     https://doi.org/10.11925/infotech.2096-3467.2020.0327
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
融合加权异构信息网络的矩阵分解推荐算法*
王根生1,2(),潘方正1
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
Matrix Factorization Algorithm with Weighted Heterogeneous Information Network
Wang Gensheng1,2(),Pan Fangzheng1
1School of Humanities, Jiangxi University of Finance and Economics, Nanchang 330013, China
2School of International Trade and Economics, Jiangxi University of Finance and Economics,Nanchang 330013, China
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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。

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王根生
潘方正
关键词 推荐算法矩阵分解异构信息网络元路径信息增益    
Abstract

[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.

Key wordsRecommendation Algorithms    Matrix Factorization    Heterogeneous Information Network    Meta Path    Information Gain
收稿日期: 2020-04-17      出版日期: 2020-12-25
ZTFLH:  TP393  
基金资助:*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”(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”(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”(GL19110)
通讯作者: 王根生     E-mail: wgs74@126.com
引用本文:   
王根生,潘方正. 融合加权异构信息网络的矩阵分解推荐算法*[J]. 数据分析与知识发现, 2020, 4(12): 76-84.
Wang Gensheng,Pan Fangzheng. Matrix Factorization Algorithm with Weighted Heterogeneous Information Network. Data Analysis and Knowledge Discovery, 2020, 4(12): 76-84.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0327      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I12/76
Fig.1  异构信息网络实例
Fig.2  网络模式示例
Fig.3  算法流程
Fig.4  FU_w函数
元路径 语义信息
P1=User-Film-User 两用户评价过同一部电影
P2=User-Film-Genre-Film-User 两用户评价过属于同一类型下的两部电影
P3=User-Film-Actor-Film-User 两用户评价过属于同一演员主演的两部电影
P4=User-Film-Director-Film-User 两用户评价过属于同一导演执导的两部电影
P5=User-Film-Country-Film-User 两用户评价过属于同一国家上映的两部电影
P6=User-Film-Screenwriter-Film-User 两用户评价过属于同一编剧编制的两部电影
Table 1  电影推荐异构信息中典型的元路径及其语义
实体类型 表示 数目
用户 User 2 113
电影 Film 10 197
演员 Actor 95 321
导演 Director 4 060
国家 Country 72
体裁 Genre 20
评分 Rating 855 598
Table 2  不同节点的详细信息
推荐算法 用户喜爱 用户不喜爱
推荐 TP FP
未推荐 FN TN
Table 3  混合矩阵
参数名称 默认参数值
正则化参数λ2 10-3
融合系数λ1 1.5
梯度下降学习率α 10-2
梯度下降迭代次数 300
用户和电影特征维度d 20
Table 4  实验参数设置
Fig.5  不同用户和电影特征维度下的实验结果
λ1 Precision/% Recall/% Coverage/% RMSE
0.5 26.1 9.4 38.4 0.81
1.0 27.5 10.3 39.2 0.77
1.5 28.4 11.5 40.1 0.75
2.0 27.6 11.2 39.5 0.78
Table 5  不同λ1下的实验对比结果
方式 Precision/% Recall/% Coverage/% RMSE
tanh改进方式 28.4 11.5 40.1 0.75
传统方式 27.6 10.6 39.2 0.77
Table 6  tanh改进方式与直接基于原始评分方式实验结果
算法 Precision/% Recall/% Coverage/% RMSE
本文算法 28.4 11.5 40.1 0.75
FunkSVD 24.0 6.1 35.5 0.81
HIN_UCF 26.6 9.4 38.4 0.78
Table 7  不同矩阵分解推荐算法实验结果
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