Are you talking to me? [CES 2018 tech show review]

Wilson, Jerry W

doi:10.1049/et.2018.0205

Engineering &Amp; Technology

2018

DOI: 10.1049/et.2018.0205

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Are you talking to me? [CES 2018 tech show review]

Jerry W Wilson¹

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2023

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“…Recommender systems are currently responsible for a large proportion of the content/items that people see and interact with online. For example, approximately 70% of the views on YouTube and 80% of the watched content on Netflix are based on algorithmic recommendations (Chhara 2017; Solsman 2018). However, algorithmic selections have long been criticized for creating “filter bubbles” (Pariser 2011) that confine individuals to self-confirming feedback loops, resulting in intellectual isolation, political polarization, and echo chambers (Flaxman, Goel, and Rao 2016).…”

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The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

Liu¹,

Cong²

2023

Journal of Marketing Research

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Recommender systems on online platforms are often accused of polarizing user attention and consumption. We examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users based on the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. We find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions (answer contributions) by 20% (23%). We show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, we find that users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. We explain these findings by exploring their underlying mechanism. We show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users.

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confidence: 99%

The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

Liu¹,

Cong²

2023

Journal of Marketing Research

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Are you talking to me? [CES 2018 tech show review]

Cited by 1 publication

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The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

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