集团关注以进行协作过,使用顺序反和上下文意识的属性.
Hadise Vaghari1, Mehdi Hosseinzadeh Aghdam2, Hojjat Emami3
1Department of Computer Engineering, Qeshm Branch, Islamic Azad University, Qeshm, Iran.
Scientific reports
|March 24, 2025
概括
通过整合短期和长期用户偏好,GCORec增强了推者系统. 这种新的框架提高了预测用户偏好的准确性,优于对基准数据集的现有方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 推系统对于根据过去的行为预测用户偏好至关重要.
- 协作过 (CF) 是一种常见的方法,但与个人评级变化和复杂的项目属性作斗争.
- 现有的基于注意力的方法在捕捉复杂的项目属性关系和提供全局解释方面缺乏可靠性.
研究的目的:
- 提出GCORec,一个新的框架来加强推者系统.
- 有效地整合短期和长期用户偏好.
- 克服模拟细粒度项目属性关系的局限性,并提供全面的解释.
主要方法:
- 对于复杂的项目-属性关系,GCORec使用了层次关注网络.
- 一个集团智能增强机制可以减少噪音,并强调重要特征.
- 一个专注的双向GRU模拟了长期的用户行为,并补充了协作多头注意力来影响属性.
主要成果:
- 在基准数据集上,GCORec显示了与基线方法相比的显著改进.
- 在Recall@20中实现了3.03%和1.49%的改进.
- 在不同稀疏度和域特征的数据集上,NDCG@20显示了5.88%和5.92%的改进.
结论:
- GCORec有效地整合了用户的短期和长期偏好,以提高推准确度.
- 该框架的新机制成功地捕捉了复杂的关系,并提供了强有力的解释.
- 在推系统的性能方面,GCORec 是一个显著的进步,特别是在处理复杂的用户-项目动态方面.
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