Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret
This work provides improved regret bounds for a specific bandit problem, with applications in recommendation systems and influence maximization, but is incremental as it builds on prior assumptions and methods.
The paper tackles the combinatorial multi-armed bandit problem with probabilistically triggered arms by proving that UCB and Thompson sampling policies achieve bounded regret under positive arm triggering probabilities, improving previous logarithmic and square-root bounds, and demonstrates this in a movie recommendation application.
In this paper, we study the combinatorial multi-armed bandit problem (CMAB) with probabilistically triggered arms (PTAs). Under the assumption that the arm triggering probabilities (ATPs) are positive for all arms, we prove that a class of upper confidence bound (UCB) policies, named Combinatorial UCB with exploration rate $κ$ (CUCB-$κ$), and Combinatorial Thompson Sampling (CTS), which estimates the expected states of the arms via Thompson sampling, achieve bounded regret. In addition, we prove that CUCB-$0$ and CTS incur $O(\sqrt{T})$ gap-independent regret. These results improve the results in previous works, which show $O(\log T)$ gap-dependent and $O(\sqrt{T\log T})$ gap-independent regrets, respectively, under no assumptions on the ATPs. Then, we numerically evaluate the performance of CUCB-$κ$ and CTS in a real-world movie recommendation problem, where the actions correspond to recommending a set of movies, the arms correspond to the edges between the movies and the users, and the goal is to maximize the total number of users that are attracted by at least one movie. Our numerical results complement our theoretical findings on bounded regret. Apart from this problem, our results also directly apply to the online influence maximization (OIM) problem studied in numerous prior works.