A novel user behavior prediction model based on automatic annotated behavior recognition in smart home systems

User behavior prediction has become a core element to Internet of Things (IoT) and received promising attention in the related fields. Many existing IoT systems (e.g. smart home systems) have been deployed various sensors and the user's behavior can be predicted through the sensor data. However, most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction. Therefore, it is a challenge to provide an automatic behavior prediction model based on the original sensor data. To solve the problem, this paper proposed a novel automatic annotated user behavior prediction (AAUBP) model. The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining (DVSM) behavior recognition model and behavior prediction model based on the Long Short Term Memory (LSTM) network. To evaluate the model, we performed several experiments on a real-world dataset tuning the parameters. The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.