M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation

TL;DR

The paper introduces M-scan, a novel multi-scenario recommendation model that explicitly models scenario influences and mitigates biases using causal inference, leading to improved prediction accuracy in data-scarce scenarios.

Contribution

It proposes a scenario-aware co-attention mechanism and a causal counterfactual bias eliminator to enhance multi-scenario recommendation performance.

Findings

M-scan outperforms baseline models on public datasets.

Explicit scenario modeling improves user interest extraction.

Causal bias mitigation enhances prediction accuracy.

Abstract

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.