Relative similarity based approach for improving aggregate recommendation diversity

Recommender systems solve the problem of information overload, by helping to find the most suitable items from a large set. Evaluating recommender system and made recommendations are equally important in an efficient recommender system. Though quality assessment of recommender system can be done using various measures, accuracy is the most important one. Sometimes accuracy may lead to a lack of user satisfaction since the user may not always be interested in the trending items. Diversity, one of the important aspects of the recommender system, eliminates such problems. Diversity is all about distinct recommendations, which are to be suggested to the user. This article presents a new metric relative similarity index (RSI) to improve the aggregate diversity of a system at a minimal loss of accuracy using nearest neighbor (NN) based collaborative filtering. The proposed algorithm is verified using two datasets namely Jester and Movie Lens.