Amazon uses machine learning to improve reviews and weed out fakes
Product reviews on sites like Amazon.com can have a huge impact on what consumers buy, and improving the quality of those reviews is in the best interests of just about everyone involved. That is why Amazon Inc is revitalizing its review system by using machine learning to highlight good reviews and weed out bad ones.
“The system will learn what reviews are most helpful to customers…and it improves over time,” Amazon spokeswoman Julie Law told CNET. “It’s all meant to make customer reviews more useful.”
The new system went into effect on Friday, but the change will not be immediately apparent to most users. As Law mentioned, the program’s artificial intelligence will get better as it is exposed to more and more reviews and user data.
Amazon’s system will weight reviews differently based on how recently they were posted, how helpful they are according to other users, and whether or not the reviewers are verified owners of the product.
Improving the star ratings
This change will also have an impact on a product’s 5-star rating system, which had previously been based on an average of all scores. This could be an even more significant change than weighting the reviews themselves, as the star rating is prominently visible under every product name, and users can also filter search results by star rating (3 stars & up, 4 stars & up, and so on).
Previously, the average score was often inaccurate because reviews that are marked as unhelpful still counted toward the 5-star score with as much weight as any other review. So if a user marked a product as 1 star because their package was late, which has nothing to do with the actual quality of the product, that rating would still count toward the total as much as a helpful review that described the product’s features in detail.
With the new system, however, a review that is marked as unhelpful would have less bearing on the product score.
Amazon’s new machine learning system will also help the site catch the fake reviews that some sellers use to artificially inflate their scores. These include reviews that are purchased through fake review services, some of which Amazon has sued in the past.
Photo by andreavallejos
A message from John Furrier, co-founder of SiliconANGLE:
Support our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.
- 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more
- 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network
Are you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/
About SiliconANGLE Media
SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE, theCUBE Network, theCUBE Research, CUBE365, theCUBE AI and theCUBE SuperStudios — with flagship locations in Silicon Valley and the New York Stock Exchange — SiliconANGLE Media operates at the intersection of media, technology and AI.
Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.
