In this paper, we report our recent practice at Tencent for user modeling
based on mobile app usage. User behaviors on mobile app usage, including
retention, installation, and uninstallation, can be a good indicator for both
long-term and short-term interests of users. For example, if a user installs
Snapseed recently, she might have a growing interest in photographing. Such
information is valuable for numerous downstream applications, including
advertising, recommendations, etc. Traditionally, user modeling from mobile app
usage heavily relies on handcrafted feature engineering, which requires onerous
human work for different downstream applications, and could be sub-optimal
without domain experts. However, automatic user modeling based on mobile app
usage faces unique challenges, including (1) retention, installation, and
uninstallation are heterogeneous but need to be modeled collectively, (2) user
behaviors are distributed unevenly over time, and (3) many long-tailed apps
suffer from serious sparsity. In this paper, we present a tailored
AutoEncoder-coupled Transformer Network (AETN), by which we overcome these
challenges and achieve the goals of reducing manual efforts and boosting
performance. We have deployed the model at Tencent, and both online/offline
experiments from multiple domains of downstream applications have demonstrated
the effectiveness of the output user embeddings.

使用移动应用的用户行为数据作为指标实现用户建模，通过 AutoEncoder-coupled Transformer 网络解决手工特征工程带来的人力成本问题，并展示了该网络在多个下游应用实验中嵌入用户的有效性。