Dual-disentangle Framework for Diversified Sequential Recommendation

• URL: http://arxiv.org/abs/2508.03172v1
• Date: Tue, 05 Aug 2025 07:25:56 GMT
• Title: Dual-disentangle Framework for Diversified Sequential Recommendation
• Authors: Haoran Zhang, Jingtong Liu, Jiangzhou Deng, Junpeng Guo,
• Abstract summary: We propose a model-agnostic Dual-disangling framework for Diversified Sequential Recommendation (DDSRec)<n>The framework refines user interest and intention modeling by adopting disentangling perspectives in interaction modeling and representation.
• Score: 16.688375054719767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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