RADAR: Fast Approximate Reverse Rank Queries

Abstract

Reverse k-rank queries, for a given query item, extract the top-k users whose ranking of the item (based on individual user’s preferences) is best among all the users. Such queries have recently received significant interest in diverse applications like market analysis, product placement, sales and e-commerce. Current approaches employ efficient high-dimensional data indexing techniques to prune input data points for improving query run-time. However, they fail to provide practical run-time characteristics for online and streaming scenarios, typical in such applications. This paper proposes the RADAR algorithm to efficiently compute, in real-time, approximate reverse k-rank queries. RADAR sorts the input data on each dimension (i.e., item aspects), and utilizes the ranking of a query in each of the dimensions to approximate the final ranking of the query by users based on their preferences. Empirical evaluations on real datasets demonstrates upto 50$\times$ run-time improvements over existing approaches, with a high accuracy of around $90\%$. Further, experiments on synthetic datasets showcase the scalability and efficacy of our algorithm for large scale and high-dimensional datasets.