Neural Contextual Bandits for Personalized Recommendation
Yikun Ban Yunzhe Qi Jingrui He
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.