Inductive Conformal Recommender System

TL;DR

This paper introduces an inductive conformal recommender system that quantifies recommendation certainty, improves computational efficiency, and maintains accuracy through novel nonconformity measures and theoretical analysis.

Contribution

It presents an inductive variant of conformal recommendation, analyzes new nonconformity measures, and provides theoretical and empirical validation of improved efficiency and accuracy.

Findings

Significant reduction in computation time.

Maintains high recommendation accuracy.

Theoretical bounds on error probability.

Abstract

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.