Session-based recommender systems are increasingly applied to next-item recommendations. However, existing approaches encode the session information of each user independently and do not consider the interrelationship between users. This work is based on the intuition that dynamic groups of like-minded users exist over time. By considering the impact of latent user groups, we can learn a user’s preference in a better way. To this end, we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups. Specifically, we utilize two network units to learn users’ long and short-term sessions, respectively. Meanwhile, we employ two additional units to determine the affiliation of users with specific latent groups, followed by an aggregation of these latent group representations. Finally, user preference representations are shaped comprehensively by considering all these four aspects, based on an attention mechanism. Moreover, to avoid setting the number of groups manually, we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically. Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall, mean average precision (mAP), and area under curve (AUC) metrics.

To protect consumers and those who manufacture and sell the products they enjoy, it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one. The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification. In this paper, we develop a Semi-Supervised Attention (SSA) model to work in conjunction with a large-scale multiple-source dataset named YSneaker, which consists of sneakers from various brands and their authentication results, to identify authentic sneakers. Specifically, the SSA model has a self-attention structure for different images of a labeled sneaker and a novel prototypical loss is designed to exploit unlabeled data within the data structure. The model draws on the weighted average of the output feature representations, where the weights are determined by an additional shallow neural network. This allows the SSA model to focus on the most important images of a sneaker for use in identification. A unique feature of the SSA model is its ability to take advantage of unlabeled data, which can help to further minimize the intra-class variation for more discriminative feature embedding. To validate the model, we collect a large number of labeled and unlabeled sneaker images and perform extensive experimental studies. The results show that YSneaker together with the proposed SSA architecture can identify authentic sneakers with a high accuracy rate.