An Improved Regularized Singular Value Decomposition Recommender Algorithm Based on Tag Transfer Learning
D Cente · JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY · 2013
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.