Rank aggregation for non-stationary data streams

TL;DR

This paper addresses learning in non-stationary ranking streams by generalizing the Borda algorithm, providing bounds on sample complexity, and extending weighted voting rules to handle varying reliability of rankings.

Contribution

It introduces a generalized Borda algorithm for non-stationary data, establishes sample bounds, and extends weighted voting rules to account for ranking reliability.

Findings

Generalized Borda algorithm effective for non-stationary streams

Provided bounds on sample complexity for accurate ranking

Extended weighted voting rules to incorporate ranking reliability

Abstract

We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the distribution of preferences changes over time. Our goal is to learn, in an online manner, the current distribution of rankings. The bottleneck of this process is a rank aggregation problem. We propose a generalization of the Borda algorithm for non-stationary ranking streams. Moreover, we give bounds on the minimum number of samples required to output the ground truth with high probability. Besides, we show how the optimal parameters are set. Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more \textit{reliable} than others and show that this generalization can solve the problem of rank aggregation over non-stationary data streams.