  Online real estate platforms are gaining increasing popularity, where a
central issue is to match brokers with clients for potential housing
transactions. Mainstream platforms match brokers via top-k recommendation. Yet
we observe through extensive data analysis that such top-k recommendation tends
to overload the top brokers, which notably degrades their service quality. In
this paper, we propose to avoid such overloading in broker matching via the
paradigm shift from recommendation to assignment. To this end, we design
learned assignment with contextual bandits (LACB), a data-driven capacity-aware
assignment scheme for broker matching which estimates broker-specific workload
capacity in an online fashion and assigns brokers to clients from a global
perspective to maximize the overall service quality. Extensive evaluations on
synthetic and real-world datasets from an industrial online real estate
platform validate the efficiency and effectiveness of our solution.
