Abstract
Recommender systems are deployed in electronic commerce (e-commerce) settings to help customers find products according to their preferences. Product recommendations may help buyers to save time by helping them choose from a variety of options. Recommendations that take into account the multiple attributes affecting a potential buyer's decision can be particularly useful in the context of Business-to-Consumer (B2C) electronic markets (e-markets). Nevertheless, multiattribute recommender systems are usually more sophisticated than single-attribute ones, and their implementation may prove complex to e-market system developers. This paper presents the design, development and evaluation of marService, a product recommendation service that is based on Multi-Attribute Utility Theory (MAUT). This approach studies the application of marService for providing wine recommendations in an existing e-market and presents the results of a simulation experiment. Using an appropriate simulation environment, the evaluation of several design options for a set of algorithms for multiattribute utility recommendation has taken place, on two synthetic data sets for wine evaluations. Based on the experience from this experiment, some general suggestions that may prove useful to e-market developers wishing to implement a marService are also provided.
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