Revisiting Optimal Rank Aggregation: A Dynamic Programming Approach

Abstract

Rank aggregation, that is merging multiple ranked lists, is a pivotal challenge in many information retrieval (IR) systems, especially in distributed IR and multilingual IR. From the evaluation point of view, being able to calculate the upper-bound of performance of the final aggregated list lays the ground for evaluating different aggregation strategies, independently. In this paper, we propose an algorithm based on dynamic programming which, using relevancy information, obtains the aggregated list with the maximum performance that could be possibly achieved by any aggregation strategy. We also provide a detailed proof for the optimality of the result of the algorithm. Furthermore, we demonstrate that the previous proposed algorithm fails to reach the optimal result in many circumstances, due to its greedy essence.