Finding the Most Diverse Products using Preference Queries

In this paper, given a product database and a set of customer preferences, we address the problem of discovering a bounded set of r diverse products that attract the interests of di↵erent customers. This problem finds numerous applications in electronic marketplaces, e.g., for selecting the products that are placed in the home page of an online shop. Existing approaches to tackle this problem fall short because they ignore customer preferences, and instead rely solely on products’ attributes. We model this problem as a diversity problem, where each product is represented by its reverse top-k result set, and seek r products that maximize their diversity value. Since the problem is NP-hard, we employ a greedy algorithm that takes as input the reverse top-k result sets of all candidate products. To further improve performance, we also design a more e cient approximate algorithm that does not require the computation of all reverse top-k sets. Our experimental evaluation demonstrates the performance of the proposed algorithms and quality of the selected diverse products.