Inductive conformal recommender system

Abstract

Traditional recommendation algorithms can be used to develop techniques that can help people choose desirable items of interest. However, in many real-world applications, it is important to quantify each recommendation’s (un)certainty, in addition to a set of recommendations. The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value. A significance level ɛ provides a bound ɛ on the probability of making a wrong recommendation. The conformal framework uses a key concept called nonconformity measure that measures the strangeness of an item concerning other items. One of the significant design challenges of any conformal recommendation framework is the integration of nonconformity measures with the recommendation algorithm. This paper introduces an inductive variant of a conformal recommender system. We propose and analyze different nonconformity measures in the inductive setting. In addition, we provide theoretical proofs on the error bound and time complexity. Extensive empirical analysis on seven benchmark datasets reveals that the inductive variant substantially improves the performance in computation time while preserving the accuracy.