  Traditional recommendation algorithms develop techniques that can help people
to choose desirable items. However, in many real-world applications, along with
a set of recommendations, it is also essential to quantify each
recommendation's (un)certainty. The conformal recommender system uses the
experience of a user to output a set of recommendations, each associated with a
precise confidence value. Given a significance level $\varepsilon$, it provides
a bound $\varepsilon$ on the probability of making a wrong recommendation. The
conformal framework uses a key concept called \emph{nonconformity measure} that
measures the strangeness of an item concerning other items. One of the
significant design challenges of any conformal recommendation framework is
integrating 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.
We also provide theoretical proofs on the error-bound and the time complexity.
Extensive empirical analysis on ten benchmark datasets demonstrates that the
inductive variant substantially improves the performance in computation time
while preserving the accuracy.
