Deep Learning-Assisted Citation Recommendation System Using Multi- cell RNN Approach

Abstract

A process of retrieving relevant or suitable papers that are mostly related to the research topic while carrying research knowing the behavior and patterns of citation for mapping the disciplines and subjects. This is used increasingly for judging usually takes a huge amount of effort and time. The citation recommendation is used frequently for solving this issue on suggesting the list of candidate papers automatically which may match the references of user. On applying Deep learning (DL) models in the citation recommendation system, the performance enhancement is greatly visible in various recent research studies. Hence, in this work, a deep learning-based model is employed for the citation recommendation system. Primarily, the input dataset is taken and the function words are removed using stochastic matching pattern scheme at which the library is created. Then, the canonicalization of document is carried by means of probabilistic min-match algorithm. The data is classified finally using Multi-cell RNN (Recurrent neural network) approach after which the rank is set for citation. At last, the performance is assessed for various metrics like MAP, MRR, precision, recall, and F1-measure. The obtained results are then compared with traditional models to validate and show the enhancement of proposed model over existing schemes. It is revealed from the analysis that the proposed scheme shows improvement in results.