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APP-installation information is helpful to describe the user'scharacteristics. The users with similar APPs installed might share severalcommon interests and behave similarly in some scenarios. In this work, we learna user embedding vector based on each user's APP-installation information.Since the user APP-installation embedding is learnable without dependency onthe historical intra-APP behavioral data of the user, it complements theintra-APP embedding learned within each specific APP. Thus, they considerablyhelp improve the effectiveness of the personalized advertising in each APP, andthey are particularly beneficial for the cold start of the new users in theAPP. In this paper, we formulate the APP-installation user embedding learninginto a bipartite graph embedding problem. The main challenge in learning aneffective APP-installation user embedding is the imbalanced data distribution.In this case, graph learning tends to be dominated by the popular APPs, whichbillions of users have installed. In other words, some niche/specialized APPsmight have a marginal influence on graph learning. To effectively exploit thevaluable information from the niche APPs, we decompose the APP-installationgraph into a set of subgraphs. Each subgraph contains only one APP node and theusers who install the APP. For each mini-batch, we only sample the users fromthe same subgraph in the training process. Thus, each APP can be involved inthe training process in a more balanced manner. After integrating the learnedAPP-installation user embedding into our online personal advertising platform,we obtained a considerable boost in CTR, CVR, and revenue.

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