  Huge volumes of patient queries are daily generated on online health forums,
rendering manual doctor allocation a labor-intensive task. To better help
patients, this paper studies a novel task of doctor recommendation to enable
automatic pairing of a patient to a doctor with relevant expertise. While most
prior work in recommendation focuses on modeling target users from their past
behavior, we can only rely on the limited words in a query to infer a patient's
needs for privacy reasons. For doctor modeling, we study the joint effects of
their profiles and previous dialogues with other patients and explore their
interactions via self-learning. The learned doctor embeddings are further
employed to estimate their capabilities of handling a patient query with a
multi-head attention mechanism. For experiments, a large-scale dataset is
collected from Chunyu Yisheng, a Chinese online health forum, where our model
exhibits the state-of-the-art results, outperforming baselines only consider
profiles and past dialogues to characterize a doctor.
