Session-aware recommendation is a special form of sequential recommendation, where users' previous interactions before current session are available. Recently, Recurrent Neural Network (RNN) based models are widely used in sequential recommendation tasks with great success. Previous works mainly focus on the interaction sequences of the current session without analyzing a user's long-term preferences. In this paper, we propose a joint neural network (JNN) for session-aware recommendation, which employs a Convolutional Neural Network(CNN) and a RNN to process the long-term historical interactions and the short-term sequential interactions respectively. Then, we apply a fully-connected neural network to study the complex relationship between these two types of features, which aims to generate a unified representation of the current session. Finally, a recommendation score for given items is generated by a bi-linear scheme upon the session representation. We conduct our experiments on three public datasets, showing that JNN outperforms the state-of-the-art baselines on all datasets in terms of Recall and Mean Reciprocal Rank (MRR). The outperforming results indicate that proper handling of historical interactions can improve the effectiveness of recommendation. The experimental results show that JNN is more prominent in samples with short current session or long historical interactions. INDEX TERMS Session-aware recommendation, sequential recommendation, recurrent neural networks, convolutional neural networks.

The aim of this research was to explore the critical indicators of evaluating higher education system. The data was obtained according to the available information from websites of relevant authoritative organizations like U.S. News and Universitas 21, etc. The national higher education system was evaluated by the analytic hierarchy process (AHP) model, then by further establishing the dual index evaluation model which includes the concepts of Health Index (H), Sustainability Index (S), finally, combined the two-index by Mckinsey matrix. This research ranked the sustainability and health of higher education systems from 10 countries in 2020 where it reflected that Unites States had the highest evaluation weight while India and Brazil still had room for improvement. Using India as the research subject, the health and sustainability degree of the improved higher education system were analyzed by Lanchester’s equation. According to the results of the evaluation model, a policy implementation model was proposed to ensure the healthy and sustainable development of India’s higher education system.

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