Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms

Understand

Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from periodically refreshed product catalogs.

• However, these algorithms face the so-called cold-start at the onset of the experiment due to a lack of knowledge of customer preferences for new products, requiring an initial data collection phase known as the burn-in period.
• During this period, standard MAB algorithms operate like randomized experiments, incurring large burn-in costs which scale with the large number of products.
• We attempt to reduce the burn-in by identifying that many products can be cast into two-sided products, and then naturally model the rewards of the products with a matrix, whose rows and columns represent the two sides respectively.