Long short-term enhanced memory for sequential recommendation

Sequential recommendation is a stream of studies on recommender systems, which focuses on predicting the next item a user interacts with by modeling the dynamic sequence of user-item interactions. Since being born to explore the dynamic tendency of variable-length temporal sequence, Recurrent Neural Networks (RNNs) have been paid much attention in this area. However, the inherent defects caused by the network structure of RNNs have limited their applications in sequential recommendation, which are mainly shown on two factors: RNNs tend to make point-wise predictions and ignore the collective dependencies because the temporal relationships between items change monotonically; RNNs are likely to forget the essential information during processing long sequences. To solve these problems, researchers have done much work to enhance the memory mechanism of RNNs. However, although previous RNN-based methods have achieved promising performance by taking advantage of external knowledge with other advanced techniques, the improvement of the intrinsic property of existing RNNs has not been explored, which is still challenging. Therefore, in this work, we propose a novel architecture based on Long Short-Term Memories (LSTMs), a broadly-used variant of RNNs, specific for sequential recommendation, called Long Short-Term enhanced Memory (LSTeM), which boosts the memory mechanism of original LSTMs in two ways. Firstly, we design a new structure of gates in LSTMs by introducing a "Q-K-V" triplet, a mechanism to accurately and properly model the correlation between the current item and the user's historical behaviors at each time step. Secondly, we propose a "recover gate" to remedy the inadequacy of memory caused by the forgetting mechanism, which works with a dynamic global memory embedding. Extensive experiments have demonstrated that LSTeM achieves comparable performance to the state-of-the-art methods on the challenging datasets for sequential recommendation.