正如题目所说，这篇文章学到是user profiling。而这个user profiling最后被用在了so-called user activity type prediction当中（这部分不是重点，负责形成闭环）。
具体做法，
（1）利用user-poi type关系可以为每个用户构建一个活动graph，这个graph可以用矩阵的形式表达出来（方便输入）。
（2）利用autoEncode可为上述每个用户的vec学到一个中间隐层表达，这个即使所谓的user profiling。也有一个重构的（1）中的向量
（3）当然，我们不能单纯的从（2）中得到user profiling，需要加入一些有意思的考虑，共同学习和调整这个user profiling。这个考虑就是题目中的子图结构。
（4）所谓的子图结构，我们是通过一个现有的算法挖掘出来的（deep first search）——我们把他当做真实的ground-truth。挖掘出来的子图同样可以用矩阵（向量）的形式表达出来
（5）此时我们需要学出一个生成对抗网络当中的G（generator）和D（discriminator），而这个G用来学习“（4）”中个ground-truth（输入是“（2）”中的重构的vec——与autoEncode发生了联系，当然这其中有一个pre-train的过程，输入是“（1）”中的vec），而D负责辨别这些子图结构（输入为“（4）”中生成的，和G生成的）是来自于“（4）”还是来自于G。
（6）自此，这个模型就可以一起快乐协同地学习参数了。最后回到（2）中的中间隐层user profilling可以当做用户特征去学习一个NN用来估计用户的activity type分布。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Adversarial Substructured Representation Learning for Mobile User Profiling | |||||||||||
| 文献作者 | Yanjie Fu; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Mobile User Profiling, Substructure, Representation Learning; 生成对抗网络； | |||||||||||
| 摘要描述 | |||||||||||
| Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user’s interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via representation learning with awareness of substructures in users’ behavioral graphs. Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. To this end, we first construct a graph, where a vertex is a POI category and an edge is the transition frequency of a user between two POI categories, to represent each user. We then formulate mobile user profiling as a task of representation learning from user behavioral graphs. We later develop a deep adversarial substructured learning framework for the task. This framework has two mutually-enhanced components. The first component is to preserve the structure of the entire graph, which is formulated as an encoding-decoding paradigm. In particular, the structure of the entire graph is preserved by minimizing reconstruction loss between an original graph and a reconstructed graph. The second component is to preserve the structure of subgraphs, which is formulated as a substructure detector based adversarial training paradigm. In particular, this paradigm includes a substructure detector and an adversarial trainer. Instead of using non-differentiable substructure detection algorithms, we pre-train a differentiable convolutional neural network as the detector to approximate these detection algorithms. The adversarial trainer is to match the detected substructure of the reconstructed graph to the detected substructure of the original graph. Also, we provide an effective solution for the optimization problems. Moreover, we exploit the learned representations of users for the next activity type prediction. Finally, we present extensive experimental results to demonstrate the improved performances of the proposed method. | |||||||||||
