Recommendation technologies for configurable products

Abstract

State-of-the-art recommender systems support users in the selection of items from a predefined assortment (for example, movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre sented in the form of a configuration knowledge base that de - scribes the properties of allowed instances. Although the knowledge representation used is different compared to nonconfi gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.