Recommender System for Academic Literature with Incremental Dataset

On account of the colossal expansion in the size of research paper repository, the stature of Recommender System has increased, as it can guide the researchers to find papers akin to them from this vast collection. Furthermore, the recommendation methods like collaborative-filtering or content-based do not allow the user's to provide their personalized requirements explicitly; hence the focus is shifted towards the customized Recommender Systems that can scrutinize user's preferences by contemplating their inputs. But the state-of-art recommendation techniques satisfying user's personalized requirements make a strong assumption of static dataset. So, in this work we are going to present a customized Recommender System that can acknowledge the ever growing nature of research paper repository. To accomplish this, the Efficient Incremental High-Utility Itemset Mining algorithm (EIHI), which has been recently introduced in the literature, is used which is specialized to work with dynamic datasets. Experimental results prove that the proposed system satisfies the researcher's personalized requirements and at the same time handles the incremental nature of the research paper repository efficiently.