Research on Collaborative Filtering Recommendation Algorithm Based on Matrix Decomposition Method
- Juan Li
- Corresponding Author
- Juan Li
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.41How to use a DOI?
- Matrix decomposition, Collaborative filtering, Data mining, Least squares, Personalization, Regularization.
- In order to realize the personalized recommendation of internet mass data, according to the characteristics of internet mining data set and combined with mathematical algorithms, this paper proposes a new forecasting and computing model of adding the regularization constraint and least square method based on the traditional matrix decomposition model (SVD), improving the speed and accuracy of the proposed algorithm. Matrix decomposition before and after improvement carries out experiments and results analysis with filtering recommendation algorithm, the experimental results show that the speed and accuracy of two prediction score calculation methods have some promotion after adding the regularization constraint and the least squares. After joining the regular constraints, the RMSE values obtained by MATLAB will monotonic decrease, avoiding the over fitting phenomenon and improving the calculation quality.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Juan Li PY - 2015/12 DA - 2015/12 TI - Research on Collaborative Filtering Recommendation Algorithm Based on Matrix Decomposition Method BT - 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering PB - Atlantis Press UR - https://doi.org/10.2991/icmmcce-15.2015.41 DO - https://doi.org/10.2991/icmmcce-15.2015.41 ID - Li2015/12 ER -