Acquiring Reliable Ratings from the Crowd

Abstract : We address the problem of acquiring reliable ratings of items such as restaurants or movies from the crowd. We propose a crowdsourcing platform that takes into consideration the workers' skills with respect to the items being rated and assigns workers the best items to rate. Our platform focuses on acquiring ratings from skilled workers and for items that only have a few ratings. We evaluate the effectiveness of our system using a real-world dataset about restaurants.
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https://hal.archives-ouvertes.fr/hal-02002007
Contributor : Sihem Amer-Yahia <>
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Beatrice Valeri, Shady Elbassuoni, Sihem Amer-Yahia. Acquiring Reliable Ratings from the Crowd. AAAI Conference on Human Computation and Crowdsourcing, 2015, Rome, Italy. ⟨hal-02002007⟩

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