The research reveals that more experienced consumers tend to select higher-quality products but rate them more stringently, resulting in potentially lower average ratings compared to inferior alternatives.
By analyzing data from IMDb, the researchers highlight a bias in ratings, where products of superior quality might receive less favorable ratings due to the expectations of seasoned buyers.
The proposed algorithm aims to adjust ratings effectively, addressing discrepancies in how experienced users rate quality, thus leading to a more accurate reflection of a product's worth.
The findings suggest a paradox in crowd wisdom, where better products can receive lower scores, challenging the conventional understanding of consumer reviews and ratings.
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