Provable Guarantees for General Two-sided Sequential Matching Markets

Two-sided markets have become increasingly more important during the last years, mostly because of their numerous applications in housing, labor and dating. Consumer-supplier matching platforms pose several technical challenges, specially due to the trade-off between recommending suitable suppliers to consumers and avoiding collisions among consumers' preferences. In this work, we study a general version of the two-sided sequential matching model introduced by Ashlagi et al. (2019). The setting is the following: we (the platform) offer a menu of suppliers to each consumer. Then, every consumer selects, simultaneously and independently, to match with a supplier or to remain unmatched. Suppliers observe the subset of consumers that selected them, and choose either to match a consumer or leave the system. Finally, a match takes place if both the consumer and the supplier sequentially select each other. Each agent's behavior is probabilistic and determined by a regular discrete choice model. Our objective is to choose an assortment family that maximizes the expected cardinality of the matching. Given the computational complexity of the problem, we show several provable guarantees for the general model, which in particular, significantly improve the approximation factors previously obtained

Assortment Planning From A Large Universe

Discrete choice models and the assortment optimization problem are the fundamental aspects of the broader field of revenue management, which now spans a broad array of industries such as airlines, hotels and online advertising. The main focus here is to first study the consumer preferences and their substitution behavior when they are faced with multiple options, explain those observed behaviors with mathematical models and then identify an optimal set of options to offer to maximize revenues. This dissertation enriches the choice models and assortment optimization fields by studying the setting when such options are available in multitude, either to the sellers or to the consumers to choose from. The first half of this dissertation focuses on the situation when sellers have access to a vast array of features to be chosen for products they want to offer. The second half of the dissertation focuses on the situation when customers are faced with a lot of options to choose from. This dissertation formulates concrete mathematical discrete choice models to tackle those situations, then studies the assortment optimization problem of maximizing the expected revenue resulting from these newly introduced choice models, and finally also designs efficient algorithms to solve them. Chapter 1 explores discrete choice models which capture consumer behavior and choices when faced with a set of different alternatives, and the resulting assortment optimization problem along with the different existing algorithms for solving them as well as the existing challenges therein. Chapter 2 models and solves the problem when the sellers have access to a vast array of inventory of products. Chapter 3 models dynamic preferences of consumers and the choice overload phenomenon when the customers are faced with a lot of options, and solves the ensuing optimization problem. Chapter 4 showcases the applicability and effectiveness of such models and approaches on high dimensional data from a field experiment on Flipkart, the largest e-commerce firm in India