  Traditional industrial recommenders are usually trained on a single business
domain and then serve for this domain. In large commercial platforms, however,
it is often the case that the recommenders need to make click-through rate
(CTR) predictions for multiple business domains. Different domains have
overlapping user groups and items, thus exist commonalities. Since the specific
user group may be different and the user behaviors may change within a specific
domain, different domains also have distinctions. The distinctions result in
different domain-specific data distributions, which makes it hard for a single
shared model to work well on all domains. To address the problem, we present
Star Topology Adaptive Recommender (STAR), where one model is learned to serve
all domains effectively. Concretely, STAR has the star topology, which consists
of the shared centered parameters and domain-specific parameters. The shared
parameters are used to learn commonalities of all domains and the
domain-specific parameters capture domain distinction for more refined
prediction. Given requests from different domains, STAR can adapt its
parameters conditioned on the domain. The experimental result from production
data validates the superiority of the proposed STAR model. Up to now, STAR has
been deployed in the display advertising system of Alibaba, obtaining averaging
8.0% improvement on CTR and 6.0% on RPM (Revenue Per Mille).
