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.