解耦用户兴趣与意图，提升推荐多样性。
Dual-disentangle Framework for Diversified Sequential Recommendation
- 从交互建模和表征学习双视角解耦用户兴趣与意图
- 在多个数据集上同时提升推荐准确率与多样性
- 适合需要平衡精准与新颖性的推荐系统开发者
序列推荐能预测用户随时间变化的偏好，已取得显著成果。然而，用户行为序列日益增长，且不断演化的兴趣与意图高度交织，给推荐多样性带来巨大挑战。为此，我们提出一种模型无关的双解耦框架（DDSRec），通过在交互建模和表征学习中引入解耦视角，优化用户兴趣与意图的建模，从而在序列推荐中实现准确率与多样性的平衡。在多个公开数据集上的大量实验表明，该方法在推荐准确率与多样性方面均表现优异且具有明显优势。
原文摘要 · Abstract (English)
Sequential recommendation predicts user preferences over time and has achieved remarkable success. However, the growing length of user interaction sequences and the complex entanglement of evolving user interests and intentions introduce significant challenges to diversity. To address these, we propose a model-agnostic Dual-disentangle framework for Diversified Sequential Recommendation (DDSRec). The framework refines user interest and intention modeling by adopting disentangling perspectives in interaction modeling and representation learning, thereby balancing accuracy and diversity in sequential recommendations. Extensive experiments on multiple public datasets demonstrate the effectiveness and superiority of DDSRec in terms of accuracy and diversity for sequential recommendations.
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