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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (12): 76-84    DOI: 10.11925/infotech.2096-3467.2020.0327
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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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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     
Received: 17 April 2020      Published: 25 December 2020
ZTFLH:  TP393  
Corresponding Authors: Wang Gensheng     E-mail: wgs74@126.com

Cite this article:

Wang Gensheng,Pan Fangzheng. Matrix Factorization Algorithm with Weighted Heterogeneous Information Network. Data Analysis and Knowledge Discovery, 2020, 4(12): 76-84.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0327     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I12/76

Heterogeneous Information Network Instance
Network Schema Example
Algorithm Flow
FU_w Function
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
Typical Meta Path and Its Semantics in Heterogeneous Information of Movie Recommendation
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
Details of Different Nodes
推荐算法 用户喜爱 用户不喜爱
推荐 TP FP
未推荐 FN TN
Mixed Matrix
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
Setting of Experimental Parameters
Experimental Results in Different User and Movie Feature Dimensions
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
Experimental Results with Different λ1
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
Experimental Comparison of tanh and Directly Based on the Original Scoring Methods
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
Experimental Results of Different Matrix Factorization Recommendation Algorithms
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