Journal Articles

Impact of Data Characteristics on Recommender Systems Performance

2012, ACM Transactions on Management Information Systems

Jingjing Zhang, G. Adomavicius

Abstract

This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.

Citation

Adomavicius, G., and J. Zhang (2012), "Impact of Data Characteristics on Recommender Systems Performance," ACM Transactions on Management Information Systems (TMIS), Vol. 3, No.1, April, Article 3.

Kelley School of Business

Faculty & Research