Cleverset recomendation engine uses statistical relational learning

November 8th, 2007

Technology review has a short article, A Better Recommendation Engine, on the Seattle company Cleverset that offers recommendation services for ecommerce.

“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.” (link)

Cleverset was founded in 2000 by Bruce D’Ambrosio of Oregon State University. Their approach is based on statistical relational learning.

“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.” (link)