Personalized session-based recommendation systems aim to predict the next items that a user would be interested in based on previously recorded user interactions. The task contains two connotations. Firstly, “personalized” indicates the recommendation generating process does not involve other users. A recommendation system is expected to capture the preference patterns of the target user only. Secondly, “session-based” signifies that the preference patterns are expressed by sequences of items or interactions. Personalized session-based recommendation technologies are of high practical values in many application areas as explosive volumes of information available for customers. In this research we model user-item sessions using graphs and propose a novel graph neural network based model, namely, Personalized Session-based Recommendation using Graph Attention Networks (PSR-GAT). The PSR-GAT makes preferred item predictions by exploiting not only instantaneous preference patterns in the current session, but also more generic preference patterns in users' historical records. The proposed PSR-GAT is built on an attention mechanism basis with a consideration of item transition linkage. PSR-GAT does not have assumptions of preference expressions in sessions. Extensive experiments are conducted to evaluate the PSR-GAT model using three datasets, one is a vehicle infotainment dataset provided by the Ford Motor Company, and the other two are publicly accessible datasets. The performances of the PSR-GAT are compared with seven state-of-art methods for session-based recommendations. The results attest to the effectiveness and advantages of the PSR-GAT in the personalized session-based next interested items prediction.

Human mental workload perception is an important problem with multiple applications in the areas of industry, transportation, military and medical contexts. In this paper, we present a deep learning model developed for detecting sequential driver mental workload using physiological signals. We modify a fully convolutional U-Net in order to learn from multiple temporal physiological signals as inputs. We also demonstrate that the element-wise cross-entropy loss function, which has been adopted by many other deep learning architectures for classification or segmentation purposes, is not sufficient for the sequential workload segmentation because it treats each data point as an independent classification object while ignoring the neighboring relation between the points. We introduce a new loss function that is sensitive to local continuity property in workload sequence. The proposed method is evaluated on two sets of data collected by wearable sensors to record multiple physiological signals. The effectiveness of an introduced scalar used to control the influence of the continuity-aware loss is investigated. Our experiments show that the proposed U-Net is effective in detecting sequential human mental workload. Furthermore, when the proposed continuity-aware loss is combined with the element-wise binary cross-entropy loss with the properly selected controlling scalar value, the quality of the predicted mental workload segments is significantly improved.