一个统一的框架连续推与封闭的差异增强注意力和重复探索意图建模的统一框架
Jinzhao Su1, Shiyu Liu1, Shunzhi Yang2
1School of Computer Science, South China Normal University, Guangzhou, 510000, China.
概括
本研究介绍了GDA-REIM,这是一个用于顺序推系统的新框架. 它有效地解决了注意力噪声,并区分了用户行为,大大提高了推准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 推系统是一个推系统.
背景情况:
- 序列推中的自我注意模型遭受注意噪声,并努力区分用户行为,如重复和探索.
- 现有的方法单独解决这些问题,限制了它们在联合优化中的有效性.
研究的目的:
- 提出一个统一的框架,GDA-REIM,共同解决注意力噪声和用户意图建模的连续建议.
- 在推任务中提高自我注意机制的准确性和稳定性.
主要方法:
- 引入了门式差分放大注意力 (GDAA),采用三级管道来抑制噪音和重新调整注意力信号.
- 实施分区意图评分 (PIS) 和意图歧视边际 (IDM) 损失,以明确区分重复和探索意图.
- 开发了一个统一的框架 (GDA-REIM),集成GDAA和PIS进行联合优化.
主要成果:
- 在多个数据集 (ML-1M,亚马逊视频游戏,Twitch-100k) 中,GDA-REIM表现出与强有力的基线相比的持续改善.
- 在ML-1M数据集的NDCG@10中实现了大约10%的改进.
- 联合优化方法被证明比单个解决问题的方法更有效.
结论:
- 拟议的GDA-REIM框架通过有效处理注意力噪声和用户意图模糊性,在顺序建议方面取得了重大进展.
- 联合优化denoising和意图建模对于改善推系统中的自我注意力表现至关重要.
- 该框架为具有复杂用户行为模式的现实世界推场景提供了强大的解决方案.
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