Eliciting GAI preference models with binary attributes aided by association rule mining

Generalized additive independent (GAI) preference models are, in many situations, more interesting than additive models, as GAI models allow interdependencies between attributes. However, they are more difficult to construct (elicit), not only because the number of questions needed to be asked increases, but also because we must know what the interdependencies are, i.e. the structure of the model. We introduce the use of association rules to select attributes and detect simple interactions between them during an interactive preference elicitation process that intends to build a GAI utility function for the preferences of the user of a recommender system. Using this strategy, we have built a recommender system prototype that suggests touristic sites in a city. We show that, after the elicitation, the recommendation problem can be solved as an instance of the non-linear generalized additive knapsack problem.