Adaptive Sequence Submodularity
This work addresses the challenge of making optimal sequential decisions under uncertainty, which is crucial for applications like recommendation systems, but it appears incremental as it builds on existing submodularity frameworks.
The paper tackles the problem of adaptive sequential decision-making, such as interactive recommendation, by proposing an adaptive greedy policy with strong theoretical guarantees, achieving competitive performance on Amazon product recommendation and Wikipedia link prediction tasks.
In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e.g., guiding an agent through a series of states). Not only do sequences already pose a dauntingly large search space, but we must also take into account past observations, as well as the uncertainty of future outcomes. Without further structure, finding an optimal sequence is notoriously challenging, if not completely intractable. In this paper, we view the problem of adaptive and sequential decision making through the lens of submodularity and propose an adaptive greedy policy with strong theoretical guarantees. Additionally, to demonstrate the practical utility of our results, we run experiments on Amazon product recommendation and Wikipedia link prediction tasks.