Enhanced context-aware citation recommendation with auxiliary textual information based on an auto-encoding mechanism

The process of retrieving suitable papers which are related to an interesting topic while doing research normally takes a lot of time and effort. Citation recommendation is frequently used to solve this problem by automatically suggesting a list of candidate papers that should match with the user’s references or topics of interest. Applying the recent research results of deep learning in multiple disciplines, the performance of citation recommendation systems has been significantly improved with the facilitation of powerful deep neural data analysis and representation learning techniques. However, most of the recent Neural Citation Network (NCN) model-based techniques still encounter limitations related to the capability of integrating auxiliary information to assist the citation contextual learning process. Thus, in this paper, we propose a novel context-aware NCN-based model with the extra textual data integration and Bidirectional Encoder Representations from Transformers (BERT) model to improve the performance of the citation recommendation task. To do this, an extensive deep neural auto-encoding mechanism with a self-attention-based mechanism is utilized in our proposed model to flexibly learn both associated textual and citation contextual data in the given dataset. These enriched citation contextual information representations are then utilized to improve the performance of the citation recommendation task as the end-to-end neural learning process. Extensive experiments in the standard arXiv dataset show the effectiveness and good performance of the proposed model.