Abstract 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.

Cross-modal retrieval has received considerable attention owing to its applicability to enable users to search desired information with diversified forms. Existing retrieval methods retain good performance mainly relying on complex deep neural networks and high-quality supervision signals, which deters them from real-world resource-constrained development and deployment. In this paper, we propose an effective unsupervised learning framework named JOint-teachinG (JOG) to pursue a high-performance yet light-weight cross-modal retrieval model. The key idea is to utilize the knowledge of a pre-trained model (a.k.a. the "teacher") to endow the to-be-learned model (a.k.a. the "student") with strong feature learning ability and predictive power. Considering that a teacher model serving the same task as the student is not always available, we resort to a cross-task teacher to leverage transferrable knowledge to guide student learning. To eliminate the inevitable noises in the distilled knowledge resulting from the task discrepancy, an online knowledge-refinement strategy is designed to progressively improve the quality of the cross-task knowledge in a joint-teaching manner, where a peer student is engaged. In addition, the proposed JOG learns to represent the original high-dimensional data with compact binary codes to accelerate the query processing, further facilitating resource-limited retrieval. Through extensive experiments, we demonstrate that in various network structures, the proposed method can yield promising learning results on widely-used benchmarks. The proposed research is a pioneering work for resource-constrained cross-modal retrieval, which has strong potential to be applied to on-device deployment and is hoped to pave the way for further study.