Research paper recommendation with topic analysis

With the collaborative filtering techniques becoming more and more mature, recommender systems are widely used nowadays, especially in electronic commerce and social networks. However, the utilization of recommender system in academic research itself has not received enough attention. A research paper recommender system would greatly help researchers to find the most desirable papers in their fields of endeavor. Due to the textual nature of papers, content information could be integrated into existed recommendation methods. In this paper, we proposed that by using topic model techniques to make topic analysis on research papers, we could introduce a thematic similarity measurement into a modified version of item-based recommendation approach. This novel recommendation method could considerable alleviate the cold start problem in research paper recommendation. Our experiment result shows that our approach could recommend highly relevant research papers.