用多个查询向量提升推荐系统稳定性与准确率
Multi-Item-Query Attention for Stable Sequential Recommendation
- 从用户交互中生成多个查询向量，增强模型鲁棒性
- 在主流数据集上显著提升推荐准确率
- 可直接替换现有模型，部署简单
用户交互数据固有的不稳定性与噪声给序列推荐系统带来挑战。现有掩码注意力模型依赖单个最近项作为查询，对噪声敏感，降低预测可靠性。本文提出多项目查询注意力机制（MIQ-Attn），从用户交互中构建多个多样化查询向量，有效缓解噪声影响，提升预测一致性。该机制设计为可直接替换现有单查询注意力的即插即用模块。实验表明，MIQ-Attn在基准数据集上显著提升性能。
原文摘要 · Abstract (English)
The inherent instability and noise in user interaction data challenge sequential recommendation systems. Prevailing masked attention models, relying on a single query from the most recent item, are sensitive to this noise, reducing prediction reliability. We propose the Multi-Item-Query attention mechanism (MIQ-Attn) to enhance model stability and accuracy. MIQ-Attn constructs multiple diverse query vectors from user interactions, effectively mitigating noise and improving consistency. It is designed for easy adoption as a drop-in replacement for existing single-query attention. Experiments show MIQ-Attn significantly improves performance on benchmark datasets.
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