  Multi-scenario recommendation is dedicated to retrieve relevant items for
users in multiple scenarios, which is ubiquitous in industrial recommendation
systems. These scenarios enjoy portions of overlaps in users and items, while
the distribution of different scenarios is different. The key point of
multi-scenario modeling is to efficiently maximize the use of whole-scenario
information and granularly generate adaptive representations both for users and
items among multiple scenarios. we summarize three practical challenges which
are not well solved for multi-scenario modeling: (1) Lacking of fine-grained
and decoupled information transfer controls among multiple scenarios. (2)
Insufficient exploitation of entire space samples. (3) Item's multi-scenario
representation disentanglement problem. In this paper, we propose a
Scenario-Adaptive and Self-Supervised (SASS) model to solve the three
challenges mentioned above. Specifically, we design a Multi-Layer Scenario
Adaptive Transfer (ML-SAT) module with scenario-adaptive gate units to select
and fuse effective transfer information from whole scenario to individual
scenario in a quite fine-grained and decoupled way. To sufficiently exploit the
power of entire space samples, a two-stage training process including
pre-training and fine-tune is introduced. The pre-training stage is based on a
scenario-supervised contrastive learning task with the training samples drawn
from labeled and unlabeled data spaces. The model is created symmetrically both
in user side and item side, so that we can get distinguishing representations
of items in different scenarios. Extensive experimental results on public and
industrial datasets demonstrate the superiority of the SASS model over
state-of-the-art methods. This model also achieves more than 8.0% improvement
on Average Watching Time Per User in online A/B tests.
