title: Leveraging position bias to improve peer recommendation.
abstract: 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.
authors.affiliation: USC Information Sciences Institute, Marina Del Rey, California, United States of America.
authors.forename: Kristina
authors.initials: K
authors.lastname: Lerman
authors.affiliation: Institute for Molecular Manufacturing, Palo Alto, California, United States of America.
authors.forename: Tad
authors.initials: T
authors.lastname: Hogg
mesh_terms: D001288:Attention; D006801:Humans; D010379:Peer Group; D011336:Probability; D061108:Social Media