Leveraging heterogeneous spillover effects in maximizing contextual bandit rewards

Recommender systems relying on contextual multi-armed bandits continuously improve relevant item recommendations by taking into account the contextual information. The objective of these bandit algorithms is to learn the best arm (i.e., best item to recommend) for each user and thus maximize the cumulative rewards from user engagement with the recommendations. However, current approaches ignore potential spillover between interacting users, where the action of one user can impact the actions and rewards of other users. Moreover, spillover may vary for different people based on their preferences and the closeness of ties to other users. This leads to heterogeneity in the spillover effects, i.e., the extent to which the action of one user can impact the action of another. Here, we propose a framework that allows contextual multi-armed bandits to account for such heterogeneous spillovers when choosing the best arm for each user. By experimenting on several real-world datasets using prominent linear and non-linear contextual bandit algorithms, we observe that our proposed method leads to significantly higher rewards than existing solutions that ignore spillover.