TY - JOUR
T1 - Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets
AU - Gaeta, Rossano
AU - Garetto, Michele
AU - RUFFO, Giancarlo Francesco
AU - Flammini, Alessandro
N1 - Publisher Copyright:
© 2023 Association for Computing Machinery.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - online cultural markets
KW - popularity bias
KW - ranking algorithms
KW - retrieval diversity
KW - online cultural markets
KW - popularity bias
KW - ranking algorithms
KW - retrieval diversity
UR - https://iris.uniupo.it/handle/11579/144951
U2 - 10.1145/3530790
DO - 10.1145/3530790
M3 - Article
SN - 1046-8188
SP - 1
EP - 33
JO - ACM Transactions on Information Systems
JF - ACM Transactions on Information Systems
ER -