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融合加权异构信息网络的矩阵分解推荐算法
王根生,潘方正
(江西财经大学,人文学院,南昌,330013)
(江西财经大学,国际经贸学院,南昌,330013)
Matrix factorization recommendation algorithms fusing weighted heterogeneous information network
WANG Gensheng,Pan Fangzheng
(School of Humanities, JiangXi University of Finance and Economics, Nanchang 330013, China)
(School 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】首先,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;其次,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;然后,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;最后,把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,该改进算法相比于传统矩阵分解推荐算法(FunkSVD)的准确率、召回率、覆盖率分别提高了4.4%、5.4%、4.6%,均方根误差(RMSE)下降了0.6。

【局限】当面对海量数据时,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】本文提出的算法提高了矩阵分解推荐算法的推荐质量。

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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] In order to improve the quality of recommendation, the knowledge of weighted heterogeneous information network is integrated into the matrix decomposition recommendation algorithm.

[Methods] Firstly, the heterogeneous information network is constructed, and the weight of connection is calculated by the improved tanh function; Secondly, the meta paths are selected in the network and their weights are calculated by information gain; Then, the user interest similarity under different meta paths is calculated, and then the user similarity matrix is calculated by combining the weights; Finally, the user similarity matrix is integrated into the objective function of the matrix factorization recommendation algorithm.

[Results] The experimental results on the Hetrec2011-MovieLens-2k dataset show that, compared with the traditional matrix factorization recommendation algorithm (FunkSVD), the accuracy, recall and coverage of the improved algorithm are increased by 4.4%, 5.4%, 4.6% respectively, and the root mean square error (RMSE) is reduced by 0.6.

[Limitations] When faced with massive data, the efficiency of matrix decomposition is low, and the problem of user interest drift is not considered.

[Conclusions] The algorithm proposed in this paper improves the recommendation quality of matrix decomposition recommendation algorithm.

Key words recommendation algorithms    matrix factorization    heterogeneous information network    meta path    information gain
     出版日期: 2020-10-09
ZTFLH:  TP393,G250  
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, 10.11925/infotech.2096-3467.2020.0327.
WANG Gensheng, Pan Fangzheng. Matrix factorization recommendation algorithms fusing weighted heterogeneous information network . Data Analysis and Knowledge Discovery, 0, (): 1-.
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/Y0/V/I/1
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