Color recommendation system combining design concepts with interactive customers preference modeling from context changes

Colors play an important role for customers in making decisions on what they like or dislike. Frequently, customers are overloaded by color combinations to consider and they may not have the time or knowledge to personally evaluate all these combinations in a product design. This paper proposes a color recommendation system which includes design concepts as rules constraining the interactive search made by genetic algorithms to follow customer preferences. As the search space is very large and it changes with contextual information, proposed system combines graph coloring techniques with artificial neural networks to keep color restrictions during system evolution and model the fitness function provided by customers. In order to illustrate an application of proposed system, building images are used as example. After including conceptual coloring rules for building images in the system by two different methods, a questionnaire study was used to verify which approach suggested better images according to customer preferences. Experiments demonstrate that only if contextual information is included in the learning process system predictions keep close to customer's evaluation.