To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders

Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is still limited. In this study, we substantially advance this field by identifying a major source of the problem: the single hidden state embedding and static item embeddings in the output softmax layer. Specifically, the similarity structure of the global item embeddings in the softmax layer sometimes forces the single hidden state embedding to be close to new items when copying is a better choice, while sometimes forcing the hidden state to be close to the items from the input inappropriately. To alleviate the problem, we adapt the recently-proposed softmax alternatives such as softmax-CPR to sequential recommendation tasks and demonstrate that the new softmax architectures unleash the capability of the neural encoder on learning when to copy and when to exclude the items from the input sequence. By only making some simple modifications on the output softmax layer for SASRec and GRU4Rec, softmax-CPR achieves consistent improvement in 12 datasets. With almost the same model size, our best method not only improves the average NDCG@10 of GRU4Rec in 5 datasets with duplicated items by 10% (4%-17% individually) but also improves 7 datasets without duplicated items by 24% (8%-39%)!

Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems

Given the recent deep learning advancements in face detection and recognition techniques for human faces, this paper answers the question "how well would they work for cartoons'?" - a domain that remains largely unexplored until recently, mainly due to the unavailability of large scale datasets and the failure of traditional methods on these. Our work studies and extends multiple frameworks for the aforementioned tasks. For face detection, we incorporate the Multi-task Cascaded Convolutional Network (MTCNN) architecture and contrast it with conventional methods. For face recognition, our two-fold contributions include: (i) an inductive transfer learning approach combining the feature learning capability of the Inception v3 network and the feature recognizing capability of Support Vector Machines (SVMs), (ii) a proposed Hybrid Convolutional Neural Network (HCNN) framework trained over a fusion of pixel values and 15 manually located facial keypoints. All the methods are evaluated on the Cartoon Faces in the Wild (IIIT-CFW) database. We demonstrate that the HCNN model offers stability superior to that of Inception+SVM over larger input variations, and explore the plausible architectural principles. We show that the Inception+SVM model establishes a state-of-the-art F1 score on the task of gender recognition of cartoon faces. Further, we introduce a small database hosting location coordinates of 15 points on the cartoon faces belonging to 50 public figures of the IIIT-CFW database.