Cascading Bandits for Large-Scale Recommendation Problems
This addresses scalable recommendation systems for users with cascade behavior, offering a novel method for learning attraction probabilities from features rather than per-item, which is incremental over prior work.
The paper tackles the problem of recommending K attractive items from a large set L in online learning with cascade user behavior, proposing algorithms based on linear generalization that achieve regret independent of L and outperform baselines.
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. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items $L$. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.