A Factual and Perceptional Framework for Assessing Diversity Effects of Online Recommendation Systems

Matt, Christian; Hess, Thomas; Weiß, Christian (2019). A Factual and Perceptional Framework for Assessing Diversity Effects of Online Recommendation Systems. Internet research, 29(6), pp. 1526-1550. Emerald 10.1108/INTR-06-2018-0274

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Purpose – The purpose of this paper is to explore the effects of online recommender systems (RS) on three types of diversity: algorithmic recommendation diversity, perceived recommendation diversity and sales diversity. The analysis distinguishes different recommendation algorithms and shows whether user perceptions match the actual effects of RS on sales.
Design/methodology/approach – An online experiment was conducted using a realistic shop design, various recommendation algorithms and a representative consumer sample to ensure the generalizability of the findings.
Findings – Recommendation algorithms show a differential impact on sales diversity, but only collaborative filtering can lead to higher sales diversity. However, some of these effects are subject to how much information firms have about users’ preferences. The level of recommendation diversity perceived by users does not always reflect the factual diversity effects.
Research limitations/implications – Recommendation and consumption patterns might differ for other types of products; future studies should replicate the study with search or credence goods. The authors also recommend that future research should move from taking a unidimensional measure for the assessment of diversity and employ multidimensional measures instead.
Practical implications – Online shops need to conduct a more comprehensive assessment of their RS’ effect on diversity, taking into account not only the effects on their sales distribution, but also on users’ perceptions and faith in the recommendation algorithm.
Originality/value – This study offers a framework for assessing different forms of diversity in online RS. It employs various recommendation algorithms and compares their impact using not just one but three different types of diversity measures. This helps explaining some of the contradictious findings from the previous literature.
Keywords Gini coefficient, Algorithmic recommendation diversity, Online recommender systems, Perceived recommendation diversity, Sales diversity
Paper type Research paper

Item Type:

Journal Article (Original Article)

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Information Systems > Information Management
03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Information Systems

UniBE Contributor:

Matt, Christian

Subjects:

000 Computer science, knowledge & systems
600 Technology > 650 Management & public relations
300 Social sciences, sociology & anthropology > 330 Economics

ISSN:

1066-2243

Publisher:

Emerald

Language:

English

Submitter:

Marisa Moser

Date Deposited:

27 May 2019 16:10

Last Modified:

16 Dec 2020 16:01

Publisher DOI:

10.1108/INTR-06-2018-0274

BORIS DOI:

10.7892/boris.130215

URI:

https://boris.unibe.ch/id/eprint/130215

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