  We primarily focus on the field of multi-scenario recommendation, which poses
a significant challenge in effectively leveraging data from different scenarios
to enhance predictions in scenarios with limited data. Current mainstream
efforts mainly center around innovative model network architectures, with the
aim of enabling the network to implicitly acquire knowledge from diverse
scenarios. However, the uncertainty of implicit learning in networks arises
from the absence of explicit modeling, leading to not only difficulty in
training but also incomplete user representation and suboptimal performance.
Furthermore, through causal graph analysis, we have discovered that the
scenario itself directly influences click behavior, yet existing approaches
directly incorporate data from other scenarios during the training of the
current scenario, leading to prediction biases when they directly utilize click
behaviors from other scenarios to train models. To address these problems, we
propose the Multi-Scenario Causal-driven Adaptive Network M-scan). This model
incorporates a Scenario-Aware Co-Attention mechanism that explicitly extracts
user interests from other scenarios that align with the current scenario.
Additionally, it employs a Scenario Bias Eliminator module utilizing causal
counterfactual inference to mitigate biases introduced by data from other
scenarios. Extensive experiments on two public datasets demonstrate the
efficacy of our M-scan compared to the existing baseline models.
