An enhanced matrix completion method based on non-negative latent factors for recommendation system. (1st September 2022) - Li, Ming; Sheng, Liqun; Song, Yan; Song, Jing
Abstract: Among the model-based collaborative filtering (CF) recommendation algorithms, matrix factorization (MF) technology is quite efficient. An ever-increasing focus has witnessed that the non-negative latent factor (NLF)-based MF model is superior to other state-of-art models, due to its great ability to guarantee a desirable prediction accuracy while grasping the non-negativity of the LF matrix. However, most existing NLF models have not adequately considered various LFs in different conditions, which might lead to negative impacts on the model performance. In order to address this issue, a novel NLF model, i.e., efficient NLF model (ENLF), is put forward to adequately reflect the various influences of LFs to the target matrix, thereby more freedom is introduced to find the solution of the established minimization problem. Furthermore, to alleviate the computational burden caused by the introduction of more latent factors, a so-called momentum-based additive gradient descent (MAGD) algorithm is employed to learn the model, where the truncating strategy is utilized during the update so as to guarantee the non-negativity of LFs. Finally, the empirical results on six real industrial data sets show that the ENLF model based on MAGD can achieve a desirable performance with relatively low time consumption. Highlights: A new non-negative latent factor model is proposed. Various non-negative latent factors are taken into consideration. We adopt a momentum additive gradient descent method to accelerate the learning. A truncating strategy is used to guarantee the non-negativity of latent factors. Empirical studies on public datasets demonstrate the effectiveness of our models.