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使用持续时间计数矩阵进行性别意识的用户配置:一种新的方法来增强内容推系统
Ali Alqazzaz1, Zunaira Anwar2, Mahmood Ul Hassan3
1College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia.
PloS one
|April 1, 2025
概括
本研究引入了一个持续计数矩阵 (DCM),通过通过观看时间持续时间分析长期用户行为来改进个性化的建议. 新的DCM技术在准确性和相关性方面明显优于现有方法.
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:
- 推系统对于用户体验和内容发现至关重要.
- 传统方法往往无法适应不断变化的用户偏好,导致不相关的建议.
- 捕捉长期的用户行为对于有效的个性化至关重要.
研究的目的:
- 开发一种创新的方法,使用观看时间持续时间来提供个性化的建议.
- 解决现有的推系统在捕捉动态用户兴趣方面的局限性.
- 通过更准确,更适应的建议来增强内容发现.
主要方法:
- 介绍时间计数矩阵 (DCM) 技术.
- DCM包括动态配置文件构建的用户配置文件 (DCM-UP) 和协作过的用户相似性 (DCM-US).
- 使用基于矩阵的表示和动态更新来反映不断变化的用户偏好.
主要成果:
- 与最先进的方法相比,DCM方法表现出明显的优异性.
- 在JAWWY的真实世界数据集上进行评估,显示了精度,回忆,F1得分和准确性的改进.
- 该技术有效地捕捉和预测长期的用户行为,以实现卓越的个性化.
结论:
- 拟议的DCM技术为个性化推提供了一种优越的方法.
- 利用观看时间的持续时间有效地模拟了长期的用户参与.
- 这种方法导致更准确,更适应,更相关的内容发现.
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