Cascading Bandits for Large-Scale Recommendation Problems

Understand

Most recommender systems recommend a list of items.

• The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest.
• This type of user behavior can be modeled by the cascade model.
• In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend $K$ most attractive items from a large set of $L$ candidate items.