  Recommender systems (RSs) are essential for e-commerce platforms to help meet
the enormous needs of users. How to capture user interests and make accurate
recommendations for users in heterogeneous e-commerce scenarios is still a
continuous research topic. However, most existing studies overlook the
intrinsic association of the scenarios: the log data collected from platforms
can be naturally divided into different scenarios (e.g., country, city,
culture).
  We observed that the scenarios are heterogeneous because of the huge
differences among them. Therefore, a unified model is difficult to effectively
capture complex correlations (e.g., differences and similarities) between
multiple scenarios thus seriously reducing the accuracy of recommendation
results.
  In this paper, we target the problem of multi-scenario recommendation in
e-commerce, and propose a novel recommendation model named Scenario-aware
Mutual Learning (SAML) that leverages the differences and similarities between
multiple scenarios. We first introduce scenario-aware feature representation,
which transforms the embedding and attention modules to map the features into
both global and scenario-specific subspace in parallel. Then we introduce an
auxiliary network to model the shared knowledge across all scenarios, and use a
multi-branch network to model differences among specific scenarios. Finally, we
employ a novel mutual unit to adaptively learn the similarity between various
scenarios and incorporate it into multi-branch network. We conduct extensive
experiments on both public and industrial datasets, empirical results show that
SAML consistently and significantly outperforms state-of-the-art methods.
