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Traditional recommendation algorithms develop techniques that can help peopleto choose desirable items. However, in many real-world applications, along witha set of recommendations, it is also essential to quantify eachrecommendation's (un)certainty. The conformal recommender system uses theexperience of a user to output a set of recommendations, each associated with aprecise confidence value. Given a significance level $\varepsilon$, it providesa bound $\varepsilon$ on the probability of making a wrong recommendation. Theconformal framework uses a key concept called \emph{nonconformity measure} thatmeasures the strangeness of an item concerning other items. One of thesignificant design challenges of any conformal recommendation framework isintegrating nonconformity measures with the recommendation algorithm. Thispaper introduces an inductive variant of a conformal recommender system. Wepropose and analyze different nonconformity measures in the inductive setting.We also provide theoretical proofs on the error-bound and the time complexity.Extensive empirical analysis on ten benchmark datasets demonstrates that theinductive variant substantially improves the performance in computation timewhile preserving the accuracy.

Inductive Conformal Recommender System