Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets

Author:

Gaeta Rossano1ORCID,Garetto Michele1ORCID,Ruffo Giancarlo1ORCID,Flammini Alessandro2ORCID

Affiliation:

1. Università di Torino, Dipartimento di Informatica, Turin, Italy

2. Indiana University Network Science Institute, Bloomington, Indiana, USA

Abstract

We propose a simple model of an idealized online cultural market in which N items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Funder

DARPA

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Reference54 articles.

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3. Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. In Proc. of RMSE Workshop at ACM Recsys.

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5. Balanced Allocations

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2. Ranking-based rich-get-richer processes;The Annals of Applied Probability;2023-12-01

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