Tensor-based Collaborative Filtering With Smooth Ratings Scale

TL;DR

This paper introduces a tensor-based collaborative filtering method that incorporates a ratings' similarity matrix to account for users' differing rating perceptions, aiming to improve recommendation accuracy.

Contribution

It proposes a novel approach using a ratings' similarity matrix to model dependencies between rating values, addressing systematic user rating discrepancies.

Findings

Improved recommendation quality through modeling rating dependencies.

Effective mitigation of systematic rating biases.

Enhanced collaborative filtering performance.

Abstract

Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.