Effects of popularity bias on average quality and faithfulness from model simulations. (a) Heatmap of average quality $\bar{q}$ as a function of $\alpha$ and $\beta$, showing that popularity bias can either hinder or promote average quality depending on exploration cost. (b) The location of the maximum $\bar{q}$ as a function of $\beta$ depends on $\alpha$, here shown for $\alpha = 0, 1, 2$. When $\alpha = 1$ an intermediate amount of popularity bias is optimal. (c) Faithfulness $\tau$ of the algorithm as a function of $\alpha$ and $\beta$. (d) $\tau$ as a function of $\beta$ for the same three values of $\alpha$. Standard errors are shown in panels ({b,d}) and are smaller than the markers if not visible.

How algorithmic popularity bias hinders or promotes quality

Effects of popularity bias on average quality and faithfulness from model simulations. (a) Heatmap of average quality $\bar{q}$ as a function of $\alpha$ and $\beta$, showing that popularity bias can either hinder or promote average quality depending on exploration cost. (b) The location of the maximum $\bar{q}$ as a function of $\beta$ depends on $\alpha$, here shown for $\alpha = 0, 1, 2$. When $\alpha = 1$ an intermediate amount of popularity bias is optimal. (c) Faithfulness $\tau$ of the algorithm as a function of $\alpha$ and $\beta$. (d) $\tau$ as a function of $\beta$ for the same three values of $\alpha$. Standard errors are shown in panels ({b,d}) and are smaller than the markers if not visible.

How algorithmic popularity bias hinders or promotes quality

Abstract

Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries-in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These 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.

Publication
Scientific Reports 8(1), p. 15951
Date
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