Shopping online from the comfort of your desk chair is certainly easier than traveling to a store and lugging home heavy bags. But for all its effortlessness, online shopping falls short when it comes to finding something you weren’t looking for but would like to buy. Recommendation systems, such as those built by Amazon, try to uncover these gems, but many fall short of appropriately catering to an individual shopper.
Now a Seattle-based startup called Cleverset thinks it has the secret to the next-generation recommendation system: a type of computer modeling found mainly in artificial-intelligence research labs. Cleverset’s system weighs the importance of the relationship among individual shoppers, their behavior on the site, the behavior of similar shoppers, and external factors such as seasons, holidays, and events like the Super Bowl. Using these ever-changing relationships, Cleverset’s system serves up products that are statistically likely to match what the customer will find interesting.
Online retailers can have millions of products in their warehouses, but a consumer only has a limited view of what’s available when she comes to a site, says Bruce D’Ambrosio, Cleverset’s founder and a professor of electrical engineering and computer science at Oregon State University. “You’ve got gigabytes of stuff behind your website,” he says, “and you only have a megapixel of display.” The challenge for most online stores is finding the best products and information to show in that tiny space on the screen.
Recommendation systems have been around for nearly as long as online retail sites have existed, and each varies slightly in its approach. Many systems just match products to people by looking at the products that others have bought. For instance, if you are looking at a blender, and people who bought the blender also bought a toaster oven, then the system would suggest a toaster oven to you. The problem here, says D’Ambrosio, is that all this analysis of purchases happens offline, and the system has no awareness of what a consumer is trying to accomplish at that specific point in time.
Cleverset uses an approach called statistical relational modeling, developed in the past decade, in which each piece of information in a data set is linked together based on its relationship to every other piece of information. This contrasts with the previous view of looking at data as if in an Excel spreadsheet, where everything carries an equal weight.
Statistical relational modeling has, for the most part, stayed cooped up in research labs. It’s been used to develop technologies such as natural-language processing (to extract relationships from text), bioinformatics (to find relationships between genes and proteins), and computer vision (to help robots see scenes as collections of related items). Daphne Koller, a professor of computer science at Stanford University, says that statistical relational modeling is good in these instances because there is a lot of uncertainly within the data sets. Relationships can be established, she says, and then statistics must be used to determine the likelihood and importance of each relationship.
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