Matrix Decomposition for Recommendation System
American Journal of Software Engineering and Applications
Volume 4, Issue 4, August 2015, Pages: 65-70
Received: Jun. 11, 2015;
Accepted: Jun. 24, 2015;
Published: Jul. 4, 2015
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Jie Zhu, School of Information, Beijing Wuzi University, Beijing, China
Yiming Wei, School of Information, Beijing Wuzi University, Beijing, China
Binbin Fu, School of Information, Beijing Wuzi University, Beijing, China
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Matrix decomposition, when the rating matrix has missing values, is recognized as an outstanding technique for recommendation system. In order to approximate user-item rating matrix, we construct loss function and append regularization constraint to prevent overfitting. Thus, the solution of matrix decomposition becomes an optimization problem. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two popular approaches to solve optimize problems. Alternating least squares with weighted regularization (ALS-WR) is a good parallel algorithm, which can perform independently on user-factor matrix or item-factor matrix. Based on the idea of ALS-WR algorithm, we propose a modified SGD algorithm. With experiments on testing dataset, our algorithm outperforms ALS-WR. In addition, matrix decompositions based on our optimization method have lower RMSE values than some classic collaborate filtering algorithms.
Matrix Decomposition, Regularization, Collaborative Filtering, Optimization
To cite this article
Matrix Decomposition for Recommendation System, American Journal of Software Engineering and Applications.
Vol. 4, No. 4,
2015, pp. 65-70.
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