Leveraging position bias to improve peer recommendation

With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the pres...

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Bibliographic Details
Published in:PloS one 2014-06, Vol.9 (2), p.e98914-e98914
Main Authors: Lerman, Kristina, Hogg, Tad
Format: Article
Language:English
Subjects:
Attention
Behavior
Bias
Cognitive ability
Cognitive biases
Computer and Information Sciences
Computer supported cooperative work
Digital media
Estimates
Experiments
Humans
International conferences
Peer Group
Popularity
Probability
R&D
Recommender systems
Research & development
Science
Social Media
Social networks
Social Sciences
Studies
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Summary:With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0098914