Automatic Paper Recommendation Algorithm Based on Multi-View Fusion TextRCNN

Traditional paper automatic recommendation algorithms only implement classification from the single-view perspectives, lacking feature fusion and multi-view semantic knowledge, contextual information, and long-distance dependence are not prominent. It is challenging to dig deep-level text features, thus limiting academic papers recommended accuracy. To this end, this paper proposes a paper automatic recommendation model based on multi-view fusion TextRCNN, which combines three-view features of paper including title, keywords, and abstract. This method uses the convolutional neural network（CNN）, bidirectional long and short-term memory network（BiLSTM）, and attention mechanism to build a model to realize the automatic classification and recommendation of papers in different disciplines. The experimental results show that the model has improved precision, recall, and F1-score, which is 3.40%, 3.57%, and 3.49% higher than the machine learning method on average, and it is also better than single-mode and existing deep learning methods. This method effectively utilizes multi-view knowledge and contextual semantic information to improve the accuracy of paper recommendations, thereby saving the time and energy spent by scientific researchers in retrieving required papers. Meanwhile, the multi-view fusion TextRCNN model can improve scientific researchers’efficiency and recommend academic papers that meet their research needs, which have good theoretical value and application expansion. © 2024 Publication Centre of Anhui Medical University. All rights reserved.