With the pricking of the Internet bubble, online retailers are under more pressure than ever to earn their keep. As a result, many companies are looking to sophisticated merchandising tools, such as intelligent agents that recommend products, to build customer loyalty and sales.Intelligent recommendation agents —automated recommendation systems that learn and improve over time — have traditionally had limitations. Most agents are based on collaborative filtering, which works by matching each target customer to a group of users with similar tastes — then using the group's choices to generate suggestions for the target customer. In order to come up with useful recommendations, collaborative filtering requires vast amounts of data, which many smaller businesses find difficult to obtain. Collaborative filtering relies on information from past users, so it's unlikely to recommend new or obscure products, even if they perfectly fit a customer's needs. And even under the best of circumstances, collaborative filtering sometimes generates recommendations that seem just plain bizarre.Researchers in computer science and marketing are studying a variety of methods for improving the performance of recommendation agents.
