Work

Leveraging Position Bias to Improve Peer Recommendation

Year: 2014

Type: article

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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0098914

Authors: Kristina Lerman, Tad Hogg

Institutions: University of Southern California, Institute for Molecular Manufacturing

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,382

Funders: Defense Advanced Research Projects Agency, Air Force Office of Scientific Research