  Information Retrieval (IR) practitioners often train separate ranking models
for different domains (geographic regions, languages, stores, websites,...) as
it is believed that exclusively training on in-domain data yields the best
performance when sufficient data is available. Despite their performance gains,
training multiple models comes at a higher cost to train, maintain and update
compared to having only a single model responsible for all domains. Our work
explores consolidated ranking models that serve multiple domains. Specifically,
we propose a novel architecture of Deep Domain Specialisation (DDS) to
consolidate multiple domains into a single model. We compare our proposal
against Deep Domain Adaptation (DDA) and a set of baseline for multi-domain
models. In our experiments, DDS performed the best overall while requiring
fewer parameters per domain as other baselines. We show the efficacy of our
method both with offline experimentation and on a large-scale online experiment
on Amazon customer traffic.
