Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets

被引：0

作者：

Dereventsov, Anton ^{[1
]}

Bibin, Anton ^{[2
]}

机构：

[1] Lirio LLC, Lirio AI Res, Knoxville, TN 37923 USA

[2] Skoltech, Skoltech Agro, Moscow, Russia

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW | 2022年

关键词：

personalization; recommendation; reinforcement learning; contextual bandit; simulated environment; FRAMEWORK;

D O I：

10.1109/ICDMW58026.2022.00127

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last.fm, Million Song, etc. This allows for personalization environments to be developed based on real-life data to reflect the nuanced nature of real-world user interactions. The obtained environments can be used to develop methods for solving personalization tasks, algorithm benchmarking, model simulation, and more. We demonstrate our approach with numerical examples on MovieLens and IMDb datasets.

引用

页码：979 / 984

页数：6

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