A Scale-Consistent Approach for Recommender Systems

TL;DR

This paper introduces a simple, efficient, and scale-consistent method for estimating missing ratings in recommender systems, ensuring robustness across diverse user rating scales.

Contribution

It presents a novel, theoretically consistent approach that adapts to different user rating units, improving the robustness of recommender systems.

Findings

Method is scale-consistent and robust across various rating scales

Achieves accurate estimation of unknown user ratings

Applicable across multiple domains of recommender systems

Abstract

In this paper we propose and develop a relatively simple and efficient approach for estimating unknown elements of a user-rating matrix in the context of a recommender system (RS). The critical theoretical property of the method is its consistency with respect to arbitrary units implicitly adopted by different users to construct their quantitative ratings of products. It is argued that this property is needed for robust performance accuracy across a broad spectrum of RS application domains.