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.

翻译：我们考虑的是相对于非静止排名流的学习问题。 排名可以被解释为人口偏好和非静止排名流的分布随时间而变化。 我们的目标是以在线方式了解当前排名的分布情况。 这一过程的瓶颈是一个排名汇总问题。 我们建议对非静止排名流采用博尔达算法的通用化。 此外, 我们给出了输出地面真理所需的最低样本数量的界限。 此外, 我们展示了最佳参数是如何设定的。 然后, 我们把加权投票规则(博尔达所属的家庭)的全组概括到某些排名比其他排名更适合\textit{relit}的情况, 并表明这种通用化可以解决排在非静止数据流之上的排名汇总问题 。