Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning

TL;DR

This paper proposes a method to improve user and app embeddings by decomposing user-app graphs into subgraphs, enhancing personalized advertising especially for niche apps and cold-start users.

Contribution

It introduces a graph decomposition approach to balance data influence across apps, leading to more effective embeddings and improved advertising metrics.

Findings

Significant boost in CTR, CVR, and revenue after implementation.

Effective handling of imbalanced data distribution across apps.

Enhanced cold-start user performance in personalized advertising.

Abstract

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.