AutoFM: an efficient factorization machine model via probabilistic auto-encoders

Studies show that conventional factorization machines (FMs) have low performance in capturing both local and global structures of user–item correlation simultaneously. Recently, deep neural networks (DNNs) have been applied to improve FMs. However, DNNs increase the complexity of the training process. Moreover, DNN-based FMs ignore the integration of neighborhood-based approaches. An efficient method called factorization machine model via probabilistic auto-encoders (AutoFM) is proposed to resolve this issue in the present study. The proposed AutoFM can extract non-trivial and local structures characteristics from user–user/item–item co-occurrence pairs by integrating a low-complexity probabilistic auto-encoder. Furthermore, it supports both explicit and implicit feedback datasets. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed method. The results show that the AutoFM outperforms the current state-of-the-art methods in rating prediction tasks. Compared with the DNN-based FM models, the proposed AutoFM model improves the item ranking at least 1.16%\(\sim\) 4.37%.