反对的对比,利用负面知识来进行兴趣点序列学习
Jinhui Zhu1, Xiangfeng Luo1, Xiao Wei1
1School of Computer Engineering and Science, Shanghai University, Shanghai, China.
概括
本研究介绍了ACLNK,这是一个新的兴趣点 (POI) 数据挖掘模型. 通过利用负面数据,ACLNK增强了用户偏好表示,改善了对多个下游任务的概括性.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 学习用户偏好表示对于挖掘兴趣点 (POI) 数据至关重要.
- 现有的POI序列学习方法往往缺乏概括性,并且由于端到端的培训,难以处理多个下游任务.
- 目前在POI序列学习中的对比学习方法忽视了负样本中的有价值特征.
研究的目的:
- 提出一种新的模型,Adversarial Contrastive with Leveraging Negative Knowledge (ACLNK),以提高POI序列学习的概括性和性能.
- 提高POI序列学习方法支持多个下游任务的能力.
- 在对比学习中有效地利用负样本的信息来获取POI数据.
主要方法:
- 设计了一个对抗性的泛化表示模块,以捕捉长期用户偏好,并纳入社交圈信息.
- 开发了一个负样本知识提取注意力机制,从有限的输入序列中提取全面的短期偏好.
- 将学习的短期和长期偏好集成到一个对比模块中,以生成准确的用户概括表示.
主要成果:
- 拟议的ACLNK模型在三个检查序列数据集中显示了有效性和普遍性的显著改善.
- 在两个不同的下游任务中,ACLNK的表现优于之前的先进模型.
- 广泛的实验验证了ACLNK在POI序列学习中的卓越性能.
结论:
- ACLNK通过提高概括性和性能,有效地解决了现有的POI序列学习方法的局限性.
- 该模型利用负面知识和纳入社会信息的能力增强了用户偏好表示.
- 在POI数据挖掘中,ACLNK为各种下游任务提供了强大的解决方案.
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