Promotion Analysis in Multi-Dimensional Space

Promotion is one of the key ingredients in marketing. It is often desirable to find merit in an object (e.g., product, person, organization, or service) and promote it in an appropriate community. In this paper, we propose a novel functionality, called promotion analysis through ranking, for promoting a given object by leveraging highly ranked results. Since the object may not be highly ranked in the global space, our goal is to discover promotive subspaces in which the object becomes prominent. To achieve this goal, the notion of promotiveness is formulated. We show that this functionality is practical and useful in a wide variety of applications such as business intelligence. However, computing promotive subspaces is challenging due to the explosion of search space and high aggregation cost. For efficient computation, we propose a PromoRank framework, and develop three efficient optimization techniques, namely subspace pruning, object pruning, and promotion cube, which are seamlessly integrated into the framework. Our empirical evaluation on two real data sets confirms the effectiveness of promotion analysis, and that our proposed algorithms significantly outperform baseline solutions.