A hybrid article recommendation system based on deep learning and co-publication network analytics

In recent years, with the rapid development of world wide web, researchers are spending more effort and time to reach the most relevant academic work for their studies because of the information overload. Preventing users from being distracted by a tremendous amount of publications and simplification of the research process makes recommendation systems more valuable. Traditional recommendation systems generally suffer from limited coverage, data sparsity, and cold start problem. In order to tackle these problems and achieve better performance, many recommender systems started to use neural network models. Being an effective neural network model, deep learning technology can transform article titles and abstract information into text embeddings and capture non-linear relationships between these text embeddings. In addition to deep learning on text embeddings, the relationship between authors has a huge effect on their future preferences. The research of copublication relationship with social network analysis improves the performance of the recommendation systems. In this study, the aim is to propose a hybrid article recommendation system that incorporates deep learning for article text similarity using Deep Siamese BiLSTM and social network analysis through node embeddings using co-publication and citation networks to exploit the network structure to provide benefit for recommender systems. Experiments conducted in this research show that the proposed model achieved a prediction rate of 7% on average when the number of articles to be recommended is taken as 100.