图表意图嵌入神经网络用于标签意识建议
Dongjing Wang1, Haojiang Yao2, Dongjin Yu2
1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, 650221, China.
概括
本研究介绍了图表意图嵌入神经网络 (GIENN),以改进标签意识建议. 吉恩更好地利用用户标记历史记录和交互意图,以获得更准确和可解释的结果.
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:
- 标签感知推系统通过使用标签数据来增强用户概况和项目表示.
- 深度学习模型有先进的标签意识建议,但在利用标签历史和用户意图方面存在局限性.
- 现有的方法往往缺乏解释性,因为它们忽视了用户与项目交互背后的意图.
研究的目的:
- 提出一个新的模型,图形意图嵌入神经网络 (GIENN),以解决当前标签意识推系统的局限性.
- 充分利用用户标签历史中的丰富内容和功能.
- 将用户交互意图纳入,以提高可解释性和建议准确性.
主要方法:
- 从用户标记历史中构建一个标签意识的交互图 (TAIG).
- 采用双层注意力机制来权衡图中邻近的节点和类型.
- 利用标签语义推断用户交互意图,并通过图形嵌入传播这些信息.
主要成果:
- 拟议的GIENN模型与最先进的基线相比显示出更高的性能.
- 在三个公共数据集上的实验验验证了GIENN在标签意识的top-N推任务中的有效性.
- 吉恩成功地整合了标签语义来揭示用户的意图,提高了推质量.
结论:
- 吉恩有效地解决了传统标签意识推系统的局限性.
- 该模型通过考虑用户意图和标签意义来提高推的准确性和可解释性.
- 在利用图形结构和注意力机制为个性化推方面,GIENN代表了重大进步.
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