Extended Factorization Machines for Sequential Recommendation

Users' historical activities are usually contained in real-life sequential recommendation systems to predict their future behaviors. In this situation, traditional Factorization Machines (FMs) approaches may be not suitable. Recently, a new surge of interest aims to use recurrent neural networks(RNN) to encode users' dynamic features with temporal characteristics. However, most of these works fail to reproduce computational simplicity of FMs. In this paper, we propose an architecture of extended-FM for sequential recommendation, which presents temporal feature interactions in an explicit way as traditional FM's formula. Our approach also allows us to accomplish computation of the model in linear time. Furthermore, we merge extended-FM into higher-order interaction framework without significant changes to the deeper models themselves. We conduct comprehensive experiments on two real-world datasets. The results demonstrate that extended-FM outperforms traditional FMs as well as deep learning feature combination models on sequential recommendation tasks.