Title |
Leveraging Position Bias to Improve Peer Recommendation
|
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Published in |
PLOS ONE, June 2014
|
DOI | 10.1371/journal.pone.0098914 |
Pubmed ID | |
Authors |
Kristina Lerman, Tad Hogg |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 67% |
India | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 56% |
Scientists | 4 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 2 | 3% |
Australia | 2 | 3% |
United Kingdom | 1 | 2% |
Unknown | 57 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 15 | 24% |
Researcher | 9 | 15% |
Student > Bachelor | 7 | 11% |
Student > Doctoral Student | 4 | 6% |
Professor > Associate Professor | 4 | 6% |
Other | 11 | 18% |
Unknown | 12 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 21 | 34% |
Business, Management and Accounting | 5 | 8% |
Social Sciences | 3 | 5% |
Agricultural and Biological Sciences | 2 | 3% |
Economics, Econometrics and Finance | 2 | 3% |
Other | 16 | 26% |
Unknown | 13 | 21% |