Each week, the New Yorker magazine runs a cartoon contest where readers are invited to submit a caption for a cartoon. And each week, Bob Mankoff, cartoon editor of the magazine, and his staff sort through thousands of submissions to find the funniest entry. To speed this process up and further engage readers, Mankoff and the New Yorker enlisted the help of NEXT and its active learning algorithms to help choose the winner.
Some of the most popular NEXT experiments involve collecting comparative judgments or pairwise comparisons from people to obtain rankings. For instance, many experiments involve showing participants two items at time, selected from a large list of items (e.g., products, images, sentences), and asking them which of the two is preferable. This sort of pairwise comparison has many virtues, as well as some limitations. This blog post explains the pros and cons of asking users to compare two items as opposed to asking users to rate of individual items.
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