Hybrid Real-Time Matrix Factorization for Implicit Feedback Recommendation Systems

In this paper, we present a hybrid real-time incremental stochastic gradient descent (RI-SGD) updating technique for implicit feedback matrix factorization (MF) recommendation systems. Compared with explicit feedback evaluation scores, implicit feedback data are easier to obtain but pose challenges to MF recommendation systems because of the transformation procedures from raw data to user preference scores. Another challenge for MF recommendation systems is the accuracy issue when the speed of the new input data increases. The proposed RI-SGD is designed for computationally-efficient and accurate time-variant implicit feedback MF recommendation system, which consists of alternating least squares with weight regularization in the training phase and stochastic gradient descent in the updating phase. To demonstrate the advantages of the RI-SGD updating technique in terms of computational efficiency and accuracy, we implement the proposed updating techniques in a real-time music recommendation system. Compared with the method of retraining the entire model, our numerical results show that RI-SGD approach can achieve almost the same recommendation accuracy, but requires only about 0.02% of the retraining time.