通过搜索异构图学习扩展和增强模糊查询的表示
概括
这项研究介绍了TAHAN,一个新的图表注意力网络模型,通过分析社交网络中的用户搜索行为来提高搜索查询的理解. TAHAN有效地解决了数据稀疏性和冷启动问题,以提高搜索相关性.
科学领域:
- 获取信息
- 人工智能
- 计算机科学
背景情况:
- 在线搜索引擎难以处理模两可的查询,
- 传统的方法依赖于个人或团体的搜索历史,面临新用户 (冷启动) 和不同用户意图的挑战.
研究的目的:
- 通过结合用户社交网络数据来增强搜索请求的表示性.
- 开发一个模型,尽管数据稀疏和冷启动问题,但可以准确地推断用户查询意图.
主要方法:
- 构建了一个包含朋友网络数据的用户-查询-文档异质图.
- 开发了TAHAN (类型意识异质图注意网络) 来建模行为特征和相互关系.
主要成果:
- 在提升查询意图表现方面,TAHAN 的表现优于最先进的模型.
- 在解决数据稀疏性和冷启动挑战方面表现卓越.
结论:
- 提议的TAHAN模型有效地提高了用户查询意图的理解.
- 不同质的图表注意力网络为个性化和准确的搜索结果检索提供了有希望的方法.
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