mlsanityData to 14 Oct 2026

Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets

arXiv 2210.10631 ↗First posted 12 Oct 2022cs.IR cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 12 Oct 2022.

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17 of 41 references.

References · 17

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5 more
  1. 2021Optimization Issues in KL-Constrained Approximate Policy Iterationcited by 10
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudgingcited by 4