  Industrial recommender systems usually hold data from multiple business
scenarios and are expected to provide recommendation services for these
scenarios simultaneously. In the retrieval step, the topK high-quality items
selected from a large number of corpus usually need to be various for multiple
scenarios. Take Alibaba display advertising system for example, not only
because the behavior patterns of Taobao users are diverse, but also
differentiated scenarios' bid prices assigned by advertisers vary
significantly. Traditional methods either train models for each scenario
separately, ignoring the cross-domain overlapping of user groups and items, or
simply mix all samples and maintain a shared model which makes it difficult to
capture significant diversities between scenarios. In this paper, we present
Adaptive Domain Interest network that adaptively handles the commonalities and
diversities across scenarios, making full use of multi-scenarios data during
training. Then the proposed method is able to improve the performance of each
business domain by giving various topK candidates for different scenarios
during online inference. Specifically, our proposed ADI models the
commonalities and diversities for different domains by shared networks and
domain-specific networks, respectively. In addition, we apply the
domain-specific batch normalization and design the domain interest adaptation
layer for feature-level domain adaptation. A self training strategy is also
incorporated to capture label-level connections across domains.ADI has been
deployed in the display advertising system of Alibaba, and obtains 1.8%
improvement on advertising revenue.
