# Domain Adaptation for Enterprise Email Search

## TL;DR

This paper introduces a domain adaptation method using Maximum Mean Discrepancy to improve enterprise email search quality by customizing a global model for individual enterprises, especially when data is limited.

## Contribution

It presents a novel application of MMD for domain adaptation in information retrieval, enhancing search performance across diverse enterprise domains.

## Key findings

- MMD-based adaptation improves search quality over global models.
- The approach outperforms adversarial learning baselines.
- Consistent improvements across multiple enterprise domains.

## Abstract

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.