In the dynamic landscape of online businesses, recommender systems are pivotal in enhancing user experiences. While traditional approaches have relied on static supervised learning, the quest for adaptive, user-centric recommendations has led to the emergence of the formulation of contextual bandits. This tutorial investigates the contextual bandits as a powerful framework for personalized recommendations. We delve into the challenges, advanced algorithms and theories, collaborative strategies, and open challenges and future prospects within this field. Different from existing related tutorials, (1) we focus on the exploration perspective of contextual bandits to alleviate the ``Matthew Effect'' in the recommender systems, i.e., the rich get richer and the poor get poorer, concerning the popularity of items; (2) in addition to the conventional linear contextual bandits, we will also dedicated to neural contextual bandits which have emerged as an important branch in recent years, to investigate how neural networks benefit contextual bandits for personalized recommendation both empirically and theoretically; (3) we will cover the latest topic, collaborative neural contextual bandits, to incorporate both user heterogeneity and user correlations customized for recommender system; (4) we will provide and discuss the new emerging challenges and open questions for neural contextual bandits with applications in the personalized recommendation, especially for large neural models.

翻译：在在线业务的动态格局中，推荐系统对提升用户体验至关重要。尽管传统方法依赖静态监督学习，但对自适应、以用户为中心的推荐需求的探索，催生了上下文赌博机框架的出现。本教程将上下文赌博机作为个性化推荐的有力框架进行深入研究。我们探讨了该领域的挑战、高级算法与理论、协同策略、开放挑战以及未来前景。与现有相关教程不同：（1）我们聚焦于上下文赌博机的探索视角，以缓解推荐系统中的"马太效应"——即物品流行度上的"富者愈富，穷者愈穷"现象；（2）除传统线性上下文赌博机外，我们同样致力于近年兴起的重要分支——神经上下文赌博机，从实证与理论层面探究神经网络如何助力上下文赌博机实现个性化推荐；（3）我们将涵盖最新主题——协同神经上下文赌博机，以融合定制化推荐系统中的用户异质性与用户关联性；（4）我们将提出并讨论神经上下文赌博机在个性化推荐应用中出现的新兴挑战与开放性问题，特别是针对大型神经模型的应用场景。