Abstract: 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.

Key words: paper recommendation, multi-view fusion, TextRCNN, attention mechanism, deep learning

摘要： 传统论文自动推荐算法仅从单视图角度实现分类，缺乏特征融合及多视图语义知识，上下文信息和长距离依赖利用不明显，较难挖掘到深层次文本特征，从而限制学术论文推荐的准确度。针对这些问题，提出了一种基于多视图融合TextRCNN的论文自动推荐模型，该模型融合论文标题、关键词和摘要三个视图特征，利用卷积神经网络（CNN）、双向长短时记忆网络（BiLSTM）和注意力机制构建模型，实现对不同学科方向论文的自动分类及推荐。实验结果表明，设计的论文推荐模型在精确率、召回率和[F1]值上均有所提升，比机器学习方法平均提高3.40%、3.57%和3.49%，也优于单视图和已有经典的深度学习方法。该方法有效利用多视图知识和上下文语义信息，提高论文推荐的准确率，进而节约科研工作者检索所需论文所花费时间和精力，进一步提高科研人员的效率，推荐符合其研究需求的学术论文，具有良好的学术价值和应用扩展。

关键词: 论文推荐, 多视图融合, TextRCNN, 注意力机制, 深度学习