论文标题
基于位置的多游戏土匪，带有汤普森抽样
Position-Based Multiple-Play Bandits with Thompson Sampling
论文作者
论文摘要
多游戏强盗旨在在网页上的相关位置显示相关项目。我们介绍了一种新的基于强盗的算法PB-MHB，用于使用Thompson采样框架的在线推荐系统。该算法处理由基于位置模型控制的显示设置。我们的采样方法不需要，因为输入用户在网页中查看给定位置的可能性很难获得。对模拟和真实数据集进行的实验表明，我们的方法（与先前的信息更少）相比提供了更好的建议。
Multiple-play bandits aim at displaying relevant items at relevant positions on a web page. We introduce a new bandit-based algorithm, PB-MHB, for online recommender systems which uses the Thompson sampling framework. This algorithm handles a display setting governed by the position-based model. Our sampling method does not require as input the probability of a user to look at a given position in the web page which is, in practice, very difficult to obtain. Experiments on simulated and real datasets show that our method, with fewer prior information, deliver better recommendations than state-of-the-art algorithms.
