Developing agent-based personalized recommender systems: An experimental study

The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct electronic shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such a problem is to build intelligent recommender systems to provide personalized information services. By analyzing the information provided by a customer, his browsing history, and the products he purchased through the Internet in the past, a personalized recommender system is able to reason about a customer's personal preferences and then provides the most appropriate information services to meet the needs of the customers. In this work, we develop an agent-based recommender system in which an evolutionary approach is proposed to learn a customer's preferences. In order to assess our approach, a prototype is built for DVD film recommendations. Experimental results and analysis show the promise of our approach.