论文标题
将用户应用程序分解为有效应用程序和用户嵌入学习的子图
Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning
论文作者
论文摘要
应用程序安装信息有助于描述用户的特征。安装了类似应用程序的用户可能会共享几个共同兴趣，并且在某些情况下的行为也相似。在这项工作中，我们会根据每个用户的应用程序安装信息学习用户嵌入向量。由于可以在不依赖用户的历史应用程序内行为数据的情况下学习用户应用程序安装嵌入，因此它补充了每个特定应用中学习的Intra Intra-App嵌入。因此，它们大大有助于提高每个应用程序中个性化广告的有效性，并且对应用程序中新用户的冷启动特别有益。在本文中，我们将App-Anstallation用户嵌入学习到两部分图嵌入问题中。学习有效的应用程序用户嵌入的主要挑战是数据分布不平衡。在这种情况下，图形学习往往由流行的应用程序主导，这些应用程序已安装了数十亿个用户。换句话说，某些利基/专业应用程序可能会对图形学习产生边缘影响。为了有效利用利基应用程序的有价值信息，我们将应用程序安装图分解为一组子图。每个子图仅包含一个应用程序和安装应用程序的用户。对于每个迷你批次，我们仅在培训过程中的同一子图中对用户进行采样。因此，每个应用程序都可以以更平衡的方式参与培训过程。将学习的应用程序安装用户嵌入到我们的在线个人广告平台中后，我们在CTR，CVR和收入中获得了可观的提升。
APP-installation information is helpful to describe the user's characteristics. The users with similar APPs installed might share several common interests and behave similarly in some scenarios. In this work, we learn a user embedding vector based on each user's APP-installation information. Since the user APP-installation embedding is learnable without dependency on the historical intra-APP behavioral data of the user, it complements the intra-APP embedding learned within each specific APP. Thus, they considerably help improve the effectiveness of the personalized advertising in each APP, and they are particularly beneficial for the cold start of the new users in the APP. In this paper, we formulate the APP-installation user embedding learning into a bipartite graph embedding problem. The main challenge in learning an effective APP-installation user embedding is the imbalanced data distribution. In this case, graph learning tends to be dominated by the popular APPs, which billions of users have installed. In other words, some niche/specialized APPs might have a marginal influence on graph learning. To effectively exploit the valuable information from the niche APPs, we decompose the APP-installation graph into a set of subgraphs. Each subgraph contains only one APP node and the users who install the APP. For each mini-batch, we only sample the users from the same subgraph in the training process. Thus, each APP can be involved in the training process in a more balanced manner. After integrating the learned APP-installation user embedding into our online personal advertising platform, we obtained a considerable boost in CTR, CVR, and revenue.
