TY  - JOUR
AU  - Adomavicius, Gediminas
AU  - Zhang, Jingjing
T1  - Impact of Data Characteristics on Recommender Systems Performance
JO  - ACM Trans. Manage. Inf. Syst.
PY  - 2012/04
VL  - 3
IS  - 1
SP  - 3:1
EP  - 3:17
UR  - http://doi.acm.org/10.1145/2151163.2151166
DO  - 10.1145/2151163.2151166
KW  - characteristics
KW  - dataset
KW  - model
KW  - recommender
KW  - dependence
KW  - evaluation
L1  - 
SN  - 
N1  - Impact of data characteristics on recommender systems performance
N1  - 
AB  - 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.
ER  -