通过图形卷积网络准确的多行为序列意识建议
Doyeon Kim1, Saurav Tanwar1, U Kang1
1Seoul National University, Seoul, Republic of Korea.
PloS one
|January 8, 2025
概括
本研究介绍了MBA,这是多行为推系统的新框架. 通过考虑用户行为的顺序和重要性,MBA增强了个性化的建议,优于现有的方法.
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:
- 多行为推系统利用不同的用户行为来提高推准确性.
- 现有的方法往往忽视了特定行为对用户偏好的个人影响.
- 个性化推增强了电子商务,流媒体和内容平台的用户体验.
研究的目的:
- 为多行为建议提出一个准确的框架,以捕捉行为依赖和个人的行为重要性.
- 通过从顺序行为数据中学习微妙的用户偏好来提高推性能.
主要方法:
- 开发了MBA (通过图形卷积网络进行多行为序列意识建议).
- 学习的嵌入反映了用户行为的依赖性和相对重要性.
- 采用复杂的抽样策略,考虑到训练期间行为的顺序性质.
主要成果:
- 与现有的多行为推方法相比,MBA表现优越.
- 在真实数据集上,在Hit Rate@10 (HR@10) 中实现了11.2%的显著改进,在Normalized Discounted Cumulative Gain@10 (nDCG@10) 中实现了11.4%的显著改进.
- 在推中验证了序列意识学习和行为重要性的有效性.
结论:
- 通过有效地建模用户行为序列及其重要性,MBA提供准确和个性化的建议.
- 拟议的框架推进了多行为推系统,提供了更好的用户参与和满意度.
- 强调了将顺序信息和行为权重纳入推者模型的重要性.
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