1. A method of generating a product recommendation for a product type, using a product recommendation engine including an application server, at least one graphical user interface, and a user computer, as well as a measured consumer values database, a product definition database, and a user database, the method comprising said customer computer performing the steps of: receiving at the user interface a user selection of a product type; retrieving from the product definition database a plurality of product attributes associated with the product type; receiving a sampled set of consumer values from the measured consumer values database; receiving at least one user preference corresponding to the product attributes from at least one of the user interface and the user database; calculating a plurality of consumer values based on the at least one user preference and the sampled set of consumer values by determining a likelihood function based on the at least one user preference, calculating an initial Gaussian probability density function based on the sampled set of consumer values, calculating a posterior Gaussian probability density function based on the likelihood function and the initial Gaussian probability density function, and calculating a plurality of consumer values based on the posterior Gaussian probability density function; determining at least one product recommendation based on the calculated consumer values; and providing the at least one product recommendation to the user via the user interface.