IncRMF: An Incremental Recommendation Algorithm Based on Regularized Matrix Factorization

The rating prediction algorithm based on matrix factorization is one of the research hotspots. When the training data is increased, all data needs to be retrained to improve recommendation results with new changes. When the data is large, the cost of calculation and time will be higher. This paper proposed an incremental recommendation algorithm based on regularized matrix factorization (IncRMF), the algorithm deals with new users and items ratings incrementally, get the updated data of training and ratings prediction by optimizing the previous result, and significantly reduce the amount of calculation in the process of training. Theoretical analysis and experimental results show that this method can guarantee the accuracy of prediction results (the difference of prediction error between IncRMF and normal regularized matrix factorization method not more than 1.36%), while the amount of calculation is significantly reduced.