Presentation information

General Session

General Session » [GS] J-2 Machine learning

[4O2-J-2] Machine learning: improvements of user satisfaction

Fri. Jun 7, 2019 12:00 PM - 1:20 PM Room O (Front-left room of 1F Exhibition hall)

Chair:Yoshifumi Seki Reviewer:Hidekazu Oiwa

1:00 PM - 1:20 PM

[4O2-J-2-04] EM-NMF ensemble method by weighted least squares method considering the number of evaluations

〇Yuichi Ohori1, Haruka Yamashita2, Masayuki Goto1 (1. Waseda University, 2. Sophia University)

Keywords:Matrix Factorization, Recommendation System, Ensemble, Collaborative Filtering

In recent years, the importance of recommendation system has been increasing from the development of information technology. One of the important technologies for the recommendation is collaborative filtering. In this study, we focus on EM-NMF which is an effective model for the collaborative filtering. The approach is based on matrix decomposition. Generally, evaluation values by users are biased to some number of items. therefore, EM-NMF tends to learn emphatically to items with many evaluations. The prediction accuracy of evaluation for items with a small number of evaluation data tends to be undesirable.

In this study, we propose a method to assemble two matrices; (i)predicted evaluation matrix based on the approach of items with many evaluation oriented and (ii)the matrix based on the approach of items with small number of evaluation obtained. This approach is expected to improve the prediction accuracy for the evaluation.