@inproceedings{2bc78ac707734aaf8908d39c90bc1d27,
title = "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.",
keywords = "Borda, Concept drift, Evolving preferences, Preference learning, Rank aggregation, Voting",
author = "Ekhine Irurozki and Aritz Perez and Jesus Lobo and \{Del Ser\}, Javier",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 21st Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021 ; Conference date: 13-09-2021 Through 17-09-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.1007/978-3-030-86523-8\_18",
language = "English",
isbn = "9783030865221",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "297--313",
editor = "Nuria Oliver and Fernando P{\'e}rez-Cruz and Stefan Kramer and Jesse Read and Lozano, \{Jose A.\}",
booktitle = "Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Proceedings",
}