Web Sites Learn To Make Smarter Suggestions
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. One possibility is to combine collaborative filtering (or other techniques based on customer communities) with an individual-based approach, which uses information from a single customer to figure out which product attributes she values most. In a recent computer simulation, individual-based agents initially performed relatively poorly. After about 40 trials, however, the simulated customers “purchased” products recommended by individual-based agents 80% of the time. By contrast, two common forms of collaborative filtering reached success rates of 50% to 60%.
Those results are reported in “Which Intelligent Agents Are Smarter? An Analysis of Relative Performance of Collaborative and Individual-Based Recommendation Agents,” by Manuel Aparicio, founder and chairman of Saffron Technology, based in Morrisville, North Carolina; Dan Ariely, associate professor at MIT's Sloan School of Management and chairman of Saffron's technical advisory board; and John Lynch, professor of marketing at Duke University's Fuqua School of Business.
One strategy suggested by the results, according to Ariely, might be to mix different techniques over time.“Initially providers can use collaborative-filtering approaches, but as they learn more about the individual user, they can use more and more of the individual approach,” he explains.
Alternatively, Web sites could use the individual-based approach to construct pseudo-users, or “bots,” and combine them with real customers in a collaborative-filtering system.