适应性自主监督学习用于顺序推
Xiujuan Sun1, Fuzhen Sun1, Zhiwei Zhang1
1School of Computer Science and Technology, Shandong University of Technology, China.
概括
本研究介绍了顺序推的自主监督学习 (ASLRec),这是一个新的框架,结合了对比和生成的自主监督学习方法. ASLRec通过学习更好的项目表示和减轻数据稀疏性和噪音,显著提高了顺序推性能.
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:
- 序列推模型往往缺乏足够的相互作用数据,导致稀疏性问题.
- 目前,顺序推的自主监督学习 (SSL) 方法仅限于单一方法和简单的数据增强.
- 现有的模型未能充分利用各种SSL技术和图形增强策略的联合力量,以改善对象表示学习.
研究的目的:
- 提出一个新的多任务顺序推框架,即自主监督的自适应学习顺序推 (ASLRec).
- 通过适应性地结合对比和生成方法来解决目前基于SSL的顺序推的局限性.
- 通过探索组合图形增大方案和多个损失函数来增强对象表示学习.
主要方法:
- ASLRec适应性地结合了对比和生成的自我监督学习方法.
- 该框架在图形拓和节点特征级别应用了各种扰动,以创建增强图形视图.
- 联合训练利用多个损失功能 (对比,生成,掩盖,预测) 并添加统一的噪音以减轻受欢迎偏见.
主要成果:
- 与14种竞争方法相比,ASLRec在三个基准数据集上实现了最先进的性能.
- 击中率 (HR) 提高了超过14.39%,正常化折扣累积收益 (NDCG) 增加了超过18.67%.
- 该模型有效地减轻了交互噪声和数据稀疏性,学习了更强大的项目表示.
结论:
- 拟议的ASLRec框架在顺序推任务中表现出卓越的性能.
- 多种SSL方法和图形增强策略的自适应组合对于学习更好的项目表示是有效的.
- 在顺序推系统中,ASLRec在解决数据稀疏性和噪声方面取得了重大进展.
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