Will you go where you search? A deep learning framework for estimating user search-and-go behavior
Will you go where you search? A deep learning framework for estimating user search-and-go behavior
复制标题
DOI：
10.1016/j.neucom.2020.10.001
复制
发表时间：
2020-10
期刊：
影响因子：
6
通讯作者：
Renhe Jiang;Quanjun Chen;Z. Cai;Z. Fan;Xuan Song;K. Tsubouchi;R. Shibasaki
中科院分区：
文献类型：
--
作者：
Renhe Jiang;Quanjun Chen;Z. Cai;Z. Fan;Xuan Song;K. Tsubouchi;R. Shibasaki
每天，人们将搜索引擎用于不同的目的，如研究、购物或娱乐。在搜索引擎用户的行为中，我们特别感兴趣的是搜索即走行为，这直观地对应了一个简单但具有挑战性的问题，即用户会去哪里搜索？准确估计此类行为对于互联网公司推荐兴趣点(POI)、广告和路线非常重要，对于政府和地铁公司等公共服务运营商进行交通监控、人群管理和交通调度也非常重要。因此，在本研究中，我们首先从Yahoo！收集具有链接且一致的用户ID的搜索日志数据和GPS日志数据。安装在数百万智能手机和平板电脑上的日本门户应用程序。然后，我们提出了一个框架，包括一个完整的数据处理过程和一个端到端的深度学习模型来预测用户是否会在搜索到的地点签到。具体地说，由于用户的日常活动被认为与他们未来的旅行、饮食和娱乐决策(即去不去)具有很高的相关性，深度时空交互网络(Deep时空交互网络)被精心设计来自动学习移动数据和搜索查询数据之间复杂的时空交互。基于标准度量的实验结果表明，我们提出的框架可以在多个真实世界搜索场景中获得令人满意的性能。
Every day, people are using search engines for different purposes such as research, shopping, or entertainment. Among the behaviors of search engine users, we are particularly interested in search-and-go behavior, which intuitively corresponds to a simple but challenging question, i.e., will users go where they search? Accurately estimating such behavior can be of great importance for Internet companies to recommend point-of-interest (POI), advertisement, and route, as well as for governments and public service operators like metro companies to conduct traffic monitoring, crowd management, and transportation scheduling. Therefore, in this study, we first collect search log data and GPS log data with linked and consistent user ID from Yahoo! Japan portal application installed in millions of smart-phones and tablets. Then we propose a framework including a complete data-processing procedure and an end-to-end deep learning model to predict whether a user will check-in the searched place or not. Specifically, as users’ daily activities are considered to have high correlation with their travel, eating, and recreation decision in the future (i.e., go or not), Deep Spatial–Temporal Interaction Network (DeepSTIN) is elaborately designed to automatically learn the sophisticated spatiotemporal interactions between mobility data and search query data. Experimental results based on the standard metrics demonstrate that our proposed framework can achieve satisfactory performances on multiple real-world search scenarios.