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PERSONALIZED SCHOLARLY ARTICLE RECOMMENDATIONS BASED ON THE RECURRENT NEURAL NETWORK AND PROBABILISTIC MODELS

Searching scholarly articles, i.e., research papers, on the Web is a challenge even for researchers who are familiar with the searched topics and articles of a particular domain of interest, needless to say for users who are not familiar with the areas of study. Existing research paper recommendation systems either rely on the contentbased or collaborative-filtering approach, or its hybrid model. These recommenders, however, are vulnerable to the cold-start problem, either on new users or new scholarly articles. In this paper, we propose an innovative personalized scholarly article recommendation system which suggests research papers of the same subject area using the recurrent neural network model and ranks closely related research papers using the BM25 probabilistic model. Experimental results based on the titles and abstracts of research papers extracted from ACM DL and IEEE Xplore digital library verify the merit of the proposed recommender, which outperforms recently-developed research paper recommendation systems.