Tuesday 28th February, 2012
11:00am to 11:30am
Getting training data for a recommender system is easy: if users clicked it, it’s a positive – if they didn’t, it’s a negative. … Or is it? In this talk, we use examples from production recommender systems to bring training data to the forefront: from overcoming presentation bias to the art of crowdsourcing subjective judgments to creative data exhaust exploitation and feature creation.
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