Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent

We consider a natural model of online preference aggregation, where sets of preferred items R1, R2, ..., Rt, ..., along with a demand for kt items in each Rt, appear online. Without prior knowledge of (Rt, kt), the learner maintains a ranking \pit aiming that at least kt items from Rt appear high in \pi_t. This is a fundamental problem in preference aggregation with applications to e.g., ordering product or news items in web pages based on user scrolling and click patterns. The widely studied Generalized Min-Sum-Set-Cover (GMSSC) problem serves as a formal model for the setting above. GMSSC is NP-hard and the standard application of no-regret online learning algorithms is computationally inefficient, because they operate in the space of rankings.