Understanding the Impact of Individual Users’ Rating Characteristics on Predictive Accuracy of Recommender Systems

INFORMS Journal on Computing, Forthcoming

Posted: 13 Mar 2018 Last revised: 2 Apr 2019

See all articles by Xiaoye Cheng

Xiaoye Cheng

Indiana University, Kelley School of Business, Department of Operation & Decision Technologies, Students

Jingjng Zhang

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Lu (Lucy) Yan

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Date Written: January 9, 2018

Abstract

In this study, we investigate how individual users' rating characteristics affect the user-level performance of recommendation algorithms. We measure users' rating characteristics from three perspectives: rating value, rating structure and neighborhood network embeddedness. We study how these three categories of measures influence the predictive accuracy of popular recommendation algorithms for each user. Our experiments use five real-world datasets with varying characteristics. For each individual user, we estimate the predictive accuracy of three recommendation algorithms. We then apply regression-based models to uncover the relationships between rating characteristics and recommendation performance at the individual user level. Our experimental results show consistent and significant effects of several rating measures on recommendation accuracy. Understanding how rating characteristics affect the recommendation performance at the individual user level has practical implications for the design of recommender systems.

Keywords: recommender systems, predictive accuracy, rating characteristics, rating value, rating structure, network embeddedness

Suggested Citation

Cheng, Xiaoye and Zhang, Jingjng and Yan, Lu (Lucy), Understanding the Impact of Individual Users’ Rating Characteristics on Predictive Accuracy of Recommender Systems (January 9, 2018). INFORMS Journal on Computing, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3132681

Xiaoye Cheng

Indiana University, Kelley School of Business, Department of Operation & Decision Technologies, Students ( email )

Business 670
1309 E. Tenth Street
Bloomington, IL 47401
United States

Jingjng Zhang

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

1309 E. Tenth Street
HH4143
Bloomington, IN 47401
United States

Lu (Lucy) Yan (Contact Author)

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Department of Operations and Decision Technologies
1309 E. Tenth Street
Bloomington, IN 47401
United States

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