Towards the D-Optimal Online Experiment Design for Recommender Selection

Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data. Finding the optimal online experiment is nontrivial since both the users and...

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Veröffentlicht in:	arXiv.org 2022-03
Hauptverfasser:	Xu, Da, Ruan, Chuanwei, Korpeoglu, Evren, Kumar, Sushant, Kannan Achan
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Information Retrieval Computer Science - Learning Design of experiments Recommender systems Statistics - Machine Learning
Online-Zugang:	Volltext
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