A novel hybrid approach of recommending research resources in a university digital library based on demographic clustering

Despite the widespread use of Digital Libraries (DL), the problem of users accessing information relevant to their needs still remains. This could be due to lack of attention and limitations in software capabilities of Digital Libraries to retrieve information and address user issues in the search process. Recommender Systems (RS) could be used in Digital Libraries to aid users in finding and selecting relevant information and knowledge sources. This research attempts to assist users of a University DL in retrieving their required resources by designing a recommendation system that uses academic demographic-based user clusters in combination with the resource content to find relevant items based on the user’s cluster. Previous research have been carried out to recommend scientific resources in academic domain by combining various methods. But none of them apply demographic based user clusters alongside the content-based and collaborative recommendation filtering. This method was evaluated using precision, recall and F-measure on the Knowledge Sharing Database for Ferdowsi University of Mashhad (PAD). Results show a significant improvement of 0.287 (118%) in the F-measure value, compared to the previous method used in this system.