Amazon has one of the most robust review systems for products, and the reviews – especially for popular products – can be extremely valuable. So valuable that many people read Amazon product reviews even when they do not plan on purchasing the product through Amazon.
However, for lesser known products, it can be a bit more problematic. And for products that have fewer reviews, they can be much more susceptible to fake reviews.
Amazon is upping the ante with their reviews process and how they select the reviews to display with machine learning.
“The system will learn what reviews are most helpful to customers…and it improves over time,” Amazon spokeswoman Julie Law said in an interview. “It’s all meant to make customer reviews more useful.”
The change, which started Friday, will probably go unnoticed at first, as the e-commerce giant’s new platform gradually starts altering the star ratings and top reviews on product pages. The new system will give more weight to newer reviews, reviews from verified Amazon purchasers and those that more customers vote up as being helpful.
It isn’t a stretch to consider Amazon favoring ones that have been deemed most useful, with some additional signals with the type of reviews, such as verified purchases, top reviewers. They have also recently added tags such as “Top 100 Reviewer” and “Vine Voice” while they have used the verified purchase tag for some time.
Amazon began using machine learning last Friday, and is meant to improve over time. It is only available for the US at this time, but it isn’t known when it will launch to more countries.
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