用户偏好交互融合和交换注意力图 神经网络用于推系统的推系统
Mingqi Li1, Wenming Ma1, Zihao Chu1
1School of Computer and Control Engineering, Yantai University, YanTai, 264005, China.
概括
本研究介绍了一种基于知识图的图形神经网络 (PIFSA-GNN),通过更好地利用用户数据和知识图信息来改进推系统. PIFSA-GNN提高了各种数据集的建议准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 推系统对于个性化用户体验至关重要.
- 知识图通过提供丰富的关系信息来增强建议.
- 现有的方法很难利用精细的知识图细节和用户重要性.
研究的目的:
- 提出一种基于知识图的新型图形神经网络 (PIFSA-GNN),以提高推的性能.
- 解决当前方法在细粒度知识图的利用和用户实体的重要性方面的局限性.
- 通过考虑用户偏好来增强邻近实体的聚合.
主要方法:
- 开发了基于知识图的图形神经网络PIFSA-GNN.
- 集成的用户偏好交互融合,以整合辅助用户信息.
- 实施了用户偏好交换注意力,以改进实体权重计算和聚合.
主要成果:
- 在电影,餐厅和音乐数据集上,PIFSA-GNN表现出卓越的性能.
- 与基线方法相比,观察到AUC,F1得分,Hit@1,Hit@5和Hit@10指标的显著改善.
- 在餐厅数据集上,AUC高达2.6%,F1高达7.2%,超过了最佳基线.
结论:
- PIFSA-GNN有效地利用细粒度的知识图信息和用户偏好.
- 提出的方法提高了推者系统的准确性和有效性.
- 这种方法为未来的知识图增强建议研究提供了一个有希望的方向.
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