Embedding-Level Attention and Multi-Scale Convolutional Neural Networks for Behaviour Modelling

Understanding human behaviour is a central task in intelligent environments. Understanding what the user does and how she does it allows to build more reactive and smart environments. In this paper we present a new approach to inter-activity behaviour modelling. This approach is based on the use of multi-scale convolutional neural networks to detect n-grams in action sequences and a novel method of applying soft attention mechanisms at embedding level. The proposed architecture improves our previous architecture based on recurrent networks, obtaining better result predicting the users' actions.