Concetti Chiave
M-scan is a novel multi-scenario recommendation model that explicitly extracts user interests from different scenarios and eliminates biases introduced by the scenarios themselves to enhance prediction accuracy.
Sintesi
The key highlights and insights from the content are:
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Traditional recommendation systems are limited in dealing with complex multi-scenario situations, as they only utilize data within a single scenario for model training. This leads to challenges such as data sparsity, incomplete user representations, and resource waste.
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The authors conduct a causal graph analysis and identify two key influences of scenarios on click behavior: the direct impact of scenarios on click behavior (S→Y), and the impact of scenarios on user interests which then influence click behavior (S→M→Y).
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To address these issues, the authors propose the Multi-Scenario Causal-driven Adaptive Network (M-scan) with two key modules:
- Scenario Bias Eliminator: This module models the direct impact of scenarios on click behavior (S→Y) and uses counterfactual causality to remove this bias during inference.
- Scenario-Aware Co-Attention: This module explicitly extracts user interests from other scenarios that are aligned with the current scenario's interests, improving the user representation.
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Extensive experiments on two public datasets demonstrate the effectiveness of M-scan compared to existing state-of-the-art multi-scenario recommendation models.
Statistiche
Some scenarios suffer from data sparsity, especially in the case of cold-start scenarios.
The absence of user information from other scenarios may lead to suboptimal performance and incomplete user representations.
Single-scenario models may result in resource waste, as large-scale commercial platforms often contain numerous scenarios.
Citazioni
"Multi-scenario recommendation systems [2] integrate information from multiple scenarios through collaborative modeling, thereby enhancing the accuracy and robustness of recommendation algorithms."
"Presently, mainstream approaches to multi-scenario modeling encounter two primary issues: (1) They solely focus on the relationship S→M→Y and overlook the direct influence of S→Y. (2) When considering S→M→Y, they focus on implicit model design, expecting the model to learn user interests in different scenarios, rather than explicitly modeling user interests."