Deep Bayesian Bandits: Exploring in Online Personalized Recommendations

Understand

Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias.

• This causes a newly trained model to act greedily and favor items that have already been engaged by users.
• This behavior is particularly harmful in personalised ads recommendations, as it can also cause new campaigns to remain unexplored.
• Exploration aims to address this limitation by providing new information about the environment, which encompasses user preference, and can lead to higher long-term reward.