Using a Deep Learning Model, Content Features, and Author Metadata to Recommend Research Papers

摘要

According to the Canadian Science Publishing, there are approximately 2.5 million scientific papers published each year. The huge volume of publications can be contributed to a substantial increase in the total number of academic journals, including the increasing number of predatory or fake scientific journals, which yield high volumes of poor-quality research work. The effect of this scenario is that there is an obsolete jungle of journals to flip through in searching for high-quality and relevant references for researchers, ranging from the ones who simply look for citations to cite or latest development and knowledge in a specific scientific area of study. Querying existing web search engines and research paper archived websites is not the solution to the problem, since they are m-equipped to suggest high quality publications to meet the users' information needs. In solving this problem, we propose an elegant research paper recommender, which is unique compared with existing ones, since besides considering the topics and contents of related publications, it also examines the authority and popularity of each publication to ensure its quality. Conducted empirical study shows that our recommender outperforms existing research paper recommenders and contributes to the design of searching relevant publications.