Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework-Reference-Cited by-同舟云学术

Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework

Published:2020-03 Issue:1 Volume:31 Page:76-101
ISSN:1047-7047
Container-title:Information Systems Research
language:en
Short-container-title:Information Systems Research

Author:

Zhang Jingjing¹^ORCID,Adomavicius Gediminas²^ORCID,Gupta Alok²^ORCID,Ketter Wolfgang³⁴^ORCID

Affiliation:

1. Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405;

2. Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;

3. Department of Management, Economics, and Social Sciences, University of Cologne, 50923 Cologne, Germany;

4. Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University Rotterdam, 3062 PA Rotterdam, Netherlands

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems,Management Information Systems

Reference42 articles.

1. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

2. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

3. Impact of data characteristics on recommender systems performance

4. Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects

5. Amatriain X, Basilico J (2012) Netflix recommendations: Beyond the 5 stars: Part 1. The Netflix Tech Blog. Accessed March 8, 2018, https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429.

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