COD: Iterative Utility Elicitation for Diversified Composite Recommendations

This paper studies and proposes methods for providing recommendations on composite bundles of products and services that are dynamically defined using database views extended with decision optimization based on mathematical programming. A framework is proposed for finding a diverse recommendation set when no prior knowledge on user preference is given. To support this framework, a method is developed for utility function elicitation, which is based on iteratively refining a set of axes in the n-dimensional utility space. The notion of a diverse recommendation set is refined and formalized by partitioning the recommendation space into layers that correspond to their distance to the maximal utility. In each layer, the method selects recommendations that maximize each dimension of the utility space. A preliminary experimental study is conducted, which shows that the proposed framework significantly outperforms a popular commercial system in terms of precision and recall.