论文标题
来自建议数据集的个性化任务的模拟上下文强盗
Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
论文作者
论文摘要
我们提出了一种从推荐数据集，如Movielens，Netflix，Last.FM，Million Song等生成模拟上下文的bundit环境的方法。这允许基于现实生活数据来开发个性化环境，以反映现实世界用户交互的细微差异。获得的环境可用于开发解决个性化任务，算法基准测试，模型仿真等方法。我们使用有关Movielens和IMDB数据集的数值示例演示了我们的方法。
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.
