  Recommender system of the e-commerce platform usually serves multiple
business scenarios. Multi-scenario Recommendation (MSR) is an important topic
that improves ranking performance by leveraging information from different
scenarios. Recent methods for MSR mostly construct scenario shared or specific
modules to model commonalities and differences among scenarios. However, when
the amount of data among scenarios is skewed or data in some scenarios is
extremely sparse, it is difficult to learn scenario-specific parameters well.
Besides, simple sharing of information from other scenarios may result in a
negative transfer. In this paper, we propose a unified model named
Cross-Scenario Information Interaction (CSII) to serve all scenarios by a
mixture of scenario-dominated experts. Specifically, we propose a novel method
to select highly transferable features in data instances. Then, we propose an
attention-based aggregator module, which can adaptively extract relative
knowledge from cross-scenario. Experiments on the production dataset verify the
superiority of our method. Online A/B test in Meituan Waimai APP also shows a
significant performance gain, leading to an average improvement in GMV (Gross
Merchandise Value) of 1.0% for overall scenarios.
