Domain Adaptation for Enterprise Email Search
This addresses the challenge of adapting search engines to specific enterprise needs without requiring large datasets, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of suboptimal search quality in enterprise email search when using a single global ranking model across diverse industries by proposing a domain adaptation approach that fine-tunes the global model to individual enterprises using Maximum Mean Discrepancy (MMD). The result showed that the MMD approach consistently improved search quality for multiple domains compared to the global model and competitive baselines.
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.