IMPROVING ACCURACY OF MULTI-CRITERIA COLLABORATIVE FILTERING BY NORMALIZING USER RATINGS

Year 2017, Volume: 18 Issue: 1, 225 - 237, 31.03.2017

Alper Bilge Alper Yargıç

https://doi.org/10.18038/aubtda.273802

Cited By: 3

Abstract

Multi-criteria collaborative filtering
schemes allow modeling user preferences in a more detailed manner by collecting
ratings on various aspects of a product or service. Although preferences are
expressed by numerical ratings within a predetermined scale, it is not
guaranteed that users comprehend such scale identically. As a result, profiles
of users with similar tastes might turn out to be unrelated. Besides, distinct
criteria might have different rating scales creating an essential incompatibility
with the rating schemes of users which in turn conceals proper relation between
main criterion and sub-criteria. Since users rate items based on their personal
rating habits, it is essential to determine user similarities according to
their rating patterns by normalizing ratings to an identical scale. In this
paper, two different normalization methods are studied, i.e., z-score normalization and decoupling
normalization, in order to improve accuracy of multi-criteria collaborative
filtering systems. In particular, two normalization methods are employed by
modifying the state-of-the-art memory-based multi-criteria recommender schemes
so that similarities among users are calculated based on preference models
rather than pure numerical ratings. Real world data-based experimental results
show that both methods, especially decoupling normalization method, provide
significant improvements on accuracy of estimated multi-criteria predictions
and outperform previous pure numerical ratings-based approach.

Keywords

multi-criteria collaborative filtering, z-score normalization, decoupling normalization, accuracy

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