A Parallel Paper recommender system in Big Data Scholarly

Nowadays, the quantity of data that is created is so huge. One area of the web that has seen continued growth is the online publication of research papers. Recommender systems were developed to help close the gap between information collection and analysis by filtering all of the available information to present what is most valuable to the user. Against this background, in this work, we address the problem of paper recommendation in Big Data scholarly. We proposed an approximate approach for recommending papers to researchers based on local sensitive hashing by converting the citations of papers to signatures and compare these signatures against each other to detect similar papers according to their citations. A parallel and distributed aspects of the proposal is also discussed.