  In the enterprise email search setting, the same search engine often powers
multiple enterprises from various industries: technology, education,
manufacturing, etc. However, using the same global ranking model across
different enterprises may result in suboptimal search quality, due to the
corpora differences and distinct information needs. On the other hand, training
an individual ranking model for each enterprise may be infeasible, especially
for smaller institutions with limited data. To address this data challenge, in
this paper we propose a domain adaptation approach that fine-tunes the global
model to each individual enterprise. In particular, we propose a novel
application of the Maximum Mean Discrepancy (MMD) approach to information
retrieval, which attempts to bridge the gap between the global data
distribution and the data distribution for a given individual enterprise. We
conduct a comprehensive set of experiments on a large-scale email search
engine, and demonstrate that the MMD approach consistently improves the search
quality for multiple individual domains, both in comparison to the global
ranking model, as well as several competitive domain adaptation baselines
including adversarial learning methods.
