An Improved Regularized Singular Value Decomposition Recommender Algorithm Based on Tag Transfer Learning
Fang Yao-ning① Guo Yun-fei① Ding Xue-tao② Lan Ju-long①
①(National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China) ②(School of Software, Tsinghua University, Beijing 100084, China)
Abstract：The recommender algorithm based on Regularized Singular Value Decomposition (RSVD) has significant advantages in predictive accuracy, while it is computationally intensive, which limits greatly its application to engineering projects. To address this issue, an improved algorithm based on tag transfer learning is proposed. It leverages tag information in the relatively denser auxiliary dataset to extract user/item features, which are further used in the RSVD approach in order to make recommendation in the target dataset. Experiments on MovieLens datasets show that the proposed algorithm can handle the sparsity issue effectively, achieve far better prediction results (reducing about 0.01 RMSE), and save about 50% training time at the same time.
方耀宁, 郭云飞, 丁雪涛, 兰巨龙. 一种基于标签迁移学习的改进正则化奇异值分解推荐算法[J]. 电子与信息学报, 2013, 35(12): 3046-3050.
Fang Yao-Ning, Guo Yun-Fei, Ding Xue-Tao, Lan Ju-Long. An Improved Regularized Singular Value Decomposition Recommender Algorithm Based on Tag Transfer Learning. , 2013, 35(12): 3046-3050.
2013, Vol. 35 
