Designing a Ranking-Based Expert Finder Using Recommender Algorithms
Satish Chandra, Vidushi · SSRN Electronic Journal · 2014
Due to the massive volume of the information available on the Internet, new techniques are required to process this data into knowledge. New methods are needed that can assist the user to find resources of his interest from the overwhelming amount of information available. Such systems which overcome excess information overload by doing information filtering in accordance with the user’s interest and assist him in decision-making process are called recommender systems. Recommendation systems are used in a variety of domains like recommending web pages, restaurants, television programs, movies, music and items for sale. In this study, a new approach has been proposed for the development of a recommender system which deals in a new domain, i.e., academia, giving altogether a different edge to these sorts of systems. In this paper, it has been demonstrated how a recommender system can be used in the field of academia as well. An Expertise Recommender (ER) has been developed using a personalized recommendation algorithm based on various rating parameters, consisting of five steps, namely, identify, classify, rate, rank and recommend.