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
[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.
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.
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