Modern large-scale systems such as recommender system and online advertising system
are built upon computation-intensive infrastructure. The typical objective in these …

We show that a two-sided matching platform can successfully compete by limiting the
number of choices it offers to its customers, while charging higher prices than platforms with …

Based on the success of recommender systems in e-commerce and entertainment, there is
growing interest in their use in matching markets like job search. While this holds potential …

The online knapsack problem is a classic problem in the field of online algorithms. Its
canonical version asks how to pack items of different values and weights arriving online into …

On online platforms, consumers face an abundance of options that are displayed in the form
of a position ranking. Only products placed in the first few positions are readily accessible to …

Firms seeking business partners and individuals seeking life partners face several
challenges in addition to finding available candidates. One of these challenges is …

While online platforms such as Uber, Lyft and Airbnb have upended the transportation and
accommodation industries, similar startups have struggled to realize comparable levels of …

Online marketplaces use ranking algorithms to determine the rank-ordering of items sold on
their websites. The standard practice is to determine the optimal algorithm using A/B tests …

We study dynamic pricing policies for ridesharing platforms such as Lyft and Uber. On one
hand these platforms are two-sided: this requires economic models that capture the …