mRHR: A modified reciprocal hit rank metric for ranking evaluation of multiple preferences in top-N recommender systems

Abstract

Average reciprocal hit rank (ARHR) is a commonly used metric for ranking evaluation of top-n recommender systems. However, it suffers from an important shortcoming that it cannot be applied when the user has multiple preferences at a time. In order to overcome this problem, a modified version of ARHR metric is introduced and applied to grocery shopping domain by conducting a series of experiments on real-life data. The results show that the proposed measure is feasible for ranking evaluation of Top-N recommender systems in the cases where the users have multiple preferences at a time or a specific time interval.