Recommendation Algorithm Based on Matrix SVD with Exponential Correction
Bingqian Zhang, Xinyu Zhou, Jiaming Li, Liang Li · 2020
Matrix decomposition is an important technology in collaborative filtering recommendation algorithm. Based on SVD, some improvements, e.g., RSVD algorithm with offset term and regular term, and SVD++ algorithm with explicit and implicit feedback, was proposed. Considering the influence of time factor on the recommendation process, we discussed a time factor to the algorithms, which were associated with the users, the projects and the interaction offset. Based on the different time decay characteristics of video and users, an algorithm with exponential correction called timeSVD++EXP is proposed. The results show that the proposed algorithm can improve the prediction accuracy of the model when root mean square error is used as the evaluation index. And the prediction accuracy of the model is further increased by considering the time factor and exponential correction.