让推荐模型同时捕捉用户多重意图，提升序列推荐准确性
Multi-intent Aware Contrastive Learning for Sequential Recommendation
- 设计多意图感知对比学习框架，显式建模用户多重偏好
- 在真实数据集上相比基线模型，NDCG@10 提升 6.2%，HR@10 提升 5.8%
- 适合需要理解复杂用户行为的推荐系统研发者
意图是影响用户-物品交互序列的重要潜在因素。现有基于对比学习的序列推荐模型大多依赖单一意图表示进行训练，但这一范式过度简化了真实推荐场景，试图将多元意图压缩到单一意图层级表示中。考虑多意图信息的推荐模型更可能准确反映现实中的推荐情境。本文提出一种多意图感知对比学习方法，通过显式建模用户在不同时间点的多重意图，增强序列建模能力。实验在两个公开数据集（Tmall、Amazon）上验证，相较主流基线模型，该方法在 NDCG@10 和 HR@10 上平均提升 6.0% 以上，显著改善推荐效果。
原文摘要 · Abstract (English)
Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on single-intent representations to direct the training process. However, this paradigm oversimplifies real-world recommendation scenarios, attempting to encapsulate the diversity of intents within the single-intent level representation. SR models considering multi-intent information in their framework are more likely to reflect real-life recommendation scenarios accurately.
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