5 accepted papers
Noise is a pervasive issue in recommendation systems, which can stem from user behaviors that do not align with their intentions. As a result, noise reduction has become a prominent area of research in the field of recommendation systems. However, existing noise reduction techniques in recommendatio…
Data augmentation with Mixup has been proven an effective method to regularize the current deep neural networks. Mixup generates virtual samples and corresponding labels simultaneously by linear interpolation. However, the one-stage generation paradigm and the use of linear interpolation have two de…
"The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort to iterative search methods on the training set, which consume hundreds or even thousands of GPU-hours. In this study, w…
For task-oriented dialogue systems, Natural Language Generation (NLG) is the last and vital step which aims at generating an appropriate response according to the dialogue act (DA). While end-to-end neural networks have achieved promising performances on this task, the existing models still struggle…
Session-based Social Recommendation (SSR) aims to use users’ social networks and historical sessions to provide more personalized recommendations for the current session.Unfortunately, existing SSR methods have two limitations.First, they do not screen users’ useless social relationships and noisy i…