Abstract:
APP-installation information is helpful to describe users’ characteristics. Users with similar APPs installed might share common interests and behave similarly in some sc...Show MoreMetadata
Abstract:
APP-installation information is helpful to describe users’ characteristics. Users with similar APPs installed might share 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, as graph learning tends to be dominated by the popular APPs installed by billions of users. In comparison, niche/specialized APPs might have a marginal influence o n g raph l earning. To e ffectively e xploit t he valuable information from niche APPs, we decompose the APP-installation graph into a set of subgraphs, each containing 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. A considerable increase in CTR, CVR, and revenue has been observed after integrating the learned APP-installation user embedding into our online personal advertising platform. Additionally, the embeddings learned from our design can be efficiently searched and ranked by various embedding-based retrieval techniques, in particular, the fast neural ranking approach using bipartite graphs [24] (also developed at Baidu Cognitive Computing Lab) would be naturally applicable.
Date of Conference: 17-20 December 2022
Date Added to IEEE Xplore: 26 January 2023
ISBN Information: