KDnuggets : News : 2009 : n09 : item21 < PREVIOUS | NEXT >


Subject: Combating manipulation of online product ratings

KINGSTON, R.I. -- April 28, 2009 -- As online shopping continues to grow in popularity around the globe, shoppers increasingly depend upon consumer-based rating systems that vendors like Amazon.com and eBay use to rate products and sellers. But those rating systems are easily manipulated, misleading many online shoppers and causing them to make purchases they otherwise may not.

To detect these unfair ratings, a team of engineers from the University of Rhode Island has developed several algorithms that can serve as a defense against collaborative, profit-driven manipulations of online rating systems.

"These reputation rating systems are used every day and they are highly valuable," said Yan Sun, assistant professor of computer engineering at URI. "Our algorithm is designed to improve the quality of the information in the rating systems to make them more reliable."

To demonstrate the value of online rating systems, Sun points to a recent survey that found that consumers are willing to pay at least 20 percent more for services that receive a 5-star rating than for the same service receiving a 4-star rating. Another survey concluded that eBay sellers with an established reputation could expect to earn 8 percent more revenue than new sellers marketing the same goods.


Sun, along with URI professors Steven Kay and Qing Yang and former doctoral student Yafei Yang, have merged several traditional signal processing techniques with their new algorithms into a novel integrated detection system that reduces rating bias by two-thirds.

Read more.

KDnuggets : News : 2009 : n09 : item21 < PREVIOUS | NEXT >

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