Ensemble Contextual Bandits for Personalized Recommendation

The cold-start problem has attracted extensive attention among various online services that provide personalized rec-ommendation. Many online vendors employ contextual ban-dit strategies to tackle the so-called exploration/exploitation dilemma rooted from the cold-start problem. However, due to high-dimensional user/item features and the underlying characteristics of bandit policies, it is often difficult for ser-vice providers to obtain and deploy an appropriate algorithm to achieve acceptable and robust economic profit. In this paper, we explore ensemble strategies of contextual bandit algorithms to obtain robust predicted click-through rate (CTR) of web objects. The ensemble is acquired by aggregating different pulling policies of bandit algorithms, rather than forcing the agreement of prediction results or learning a unified predictive model. To this end, we employ a meta-bandit paradigm that places a hyper bandit over the base bandits, to explicitly explore/exploit the relative importance of base bandits based on user feedbacks. Ex-tensive empirical experiments on two real-world data sets (news recommendation and online advertising) demonstrate the effectiveness of our proposed approach in terms of CTR