  In this paper, we propose a simple yet effective solution to build practical
teacher recommender systems for online one-on-one classes. Our system consists
of (1) a pseudo matching score module that provides reliable training labels;
(2) a ranking model that scores every candidate teacher; (3) a novelty boosting
module that gives additional opportunities to new teachers; and (4) a diversity
metric that guardrails the recommended results to reduce the chance of
collision. Offline experimental results show that our approach outperforms a
wide range of baselines. Furthermore, we show that our approach is able to
reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a
five-month observation on a third-party online education platform.
