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Computer Science > Information Retrieval

arXiv:2210.10631 (cs)
[Submitted on 12 Oct 2022 (v1), last revised 17 Dec 2022 (this version, v2)]

Title:Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets

Authors:Anton Dereventsov, Anton Bibin
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Abstract:We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, this http URL, 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.
Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2210.10631 [cs.IR]
  (or arXiv:2210.10631v2 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2210.10631

Submission history

From: Anton Dereventsov [view email]
[v1] Wed, 12 Oct 2022 21:53:15 UTC (609 KB)
[v2] Sat, 17 Dec 2022 16:24:33 UTC (609 KB)
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