对比式学习增强个性化交互双塔网络用于推.
Fang Yang1,2, Binghui Wang1, Pengliang Li2
1Malaysia SEGi University, Kuala Lumpur, Malaysia.
PloS one
|October 23, 2025
概括
新的对比学习增强个性化交互双塔网络 (CL-EPIDTN) 通过更好地建模用户行为和增强对不同内容和用户的表示学习来改进推系统.
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:
- 双塔检索模型对于大规模的建议是有效的,但与用户行为,个性化和长尾内容扎.
- 现有的模型缺乏动态的用户偏好跟踪和个性化的功能交互.
- 糟糕的表示学习会影响使用稀疏数据和利基项目的性能.
研究的目的:
- 提出一个新的框架,对比学习增强个性化交互双塔网络 (CL-EPIDTN),以克服当前双塔模型的局限性.
- 增强用户行为序列的建模和个性化的塔间互动.
- 改善长尾内容和低活动用户的表示学习.
主要方法:
- 集成了一个多层变压器,以捕捉动态用户偏好.
- 引入了一个双路径个性化增强机制,用于用户项目功能依赖.
- 采用了对比式学习策略,以解决数据稀疏性和改善表示学习.
主要成果:
- 与六个最先进的基线相比,CL-EPIDTN在亚马逊图书和TmallData数据集上表现优越.
- 在亚马逊书籍上获得了0.0351的成功率@10和0.1123的回忆率@50.
- 在TmallData.Data上实现了0.0901的成功率@10和0.1599的回忆率@50
结论:
- CL-EPIDTN有效地解决了推者系统中的个性化和数据稀疏性挑战.
- 拟议的框架显示了学术研究和实际应用的巨大潜力.
- 变压器和对比学习的集成提高了双塔模型的可扩展性和效率.
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