Multi-Scenario Recommendation Model
The paper introduces SASS, a scenario-adaptive self-supervised recommendation model that uses contrastive pre-training and multi-layer gating to expand global samples and transfer scene-aware parameters, enabling a single model to deliver personalized recommendations across diverse Taobao ‘SuoSuo’ scenarios while mitigating data sparsity and cross-domain challenges.
This paper presents a scenario-adaptive and self-supervised model (SASS) for multi-scenario personalized recommendation, addressing challenges in content recommendation across diverse scenarios. The model integrates global sample expansion through contrastive learning and scene-specific adaptation via multi-layer gate mechanisms. Key contributions include a two-stage training framework, scene-aware parameter transfer, and cross-domain modeling between content and commerce domains. Experimental results demonstrate improved recommendation performance across five core scenarios in Taobao's 'SuoSuo' platform.
The framework combines scene-contrastive pre-training with adaptive transfer modules to handle data sparsity and domain differences. It enables one-model deployment across multiple scenarios while maintaining scene-specific personalization. Applications extend to cold start mitigation, cross-domain modeling, and interactive engagement optimization.
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