[KDD 2020] General-Purpose User Embeddings based on Mobile App Usage - Crossminds
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[KDD 2020] General-Purpose User Embeddings based on Mobile App Usage
Aug 13, 2020
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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 longtailed 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.
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 longtailed 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.
SIGKDD_2020Applied Research
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