Rank Aggregation for Non-Stationary Data Streams
Abstract
The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner. The bottleneck of this process is a rank aggregation problem. We propose a generalization of the Borda algorithm for non-stationary ranking streams. As a main result, we bound 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 reliable than others. We show that, under mild assumptions, this generalization can solve the problem of rank aggregation over non-stationary data streams.
Cite
Text
Irurozki et al. "Rank Aggregation for Non-Stationary Data Streams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86523-8_18Markdown
[Irurozki et al. "Rank Aggregation for Non-Stationary Data Streams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/irurozki2021ecmlpkdd-rank/) doi:10.1007/978-3-030-86523-8_18BibTeX
@inproceedings{irurozki2021ecmlpkdd-rank,
title = {{Rank Aggregation for Non-Stationary Data Streams}},
author = {Irurozki, Ekhine and Pérez, Aritz and Lobo, Jesus L. and Del Ser, Javier},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2021},
pages = {297-313},
doi = {10.1007/978-3-030-86523-8_18},
url = {https://mlanthology.org/ecmlpkdd/2021/irurozki2021ecmlpkdd-rank/}
}