Recommending the Perfect Match: Performance Improvement of Human Computation by Collaborative Filtering
Date Issued
2010
Date
2010
Author(s)
Lee, Jong-Chuan
Abstract
With the popularity of the Internet, humancomputation came into being. However,
there are 20 million Internet users from the whole world, each with a different
background skills. Current human computation system with random division of labor
making users hard to reach a consensus with each other and contribute on their
expert domains reduces the ef⣸00;ciency of the system.
To solve this problem, we use the collaborative f iltering approach of recommendersystem
to match the appropriate partner and the problem in their expert domain with user
history. Based on professor Luis von Ahn’s ESPGame, we design the experiment on
the crowdsourcing platform – AmazonMechanicalTurk. And total 910 users involve
and 4757 games are record. Experimental results demonstrate that the recommender
system provide users the perfect match and improve the ef⣸00;ciency of human computation.
Subjects
human computation
collaborative filtering
recommender system
Type
thesis
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