[KDD 2020] Time-Aware User Embeddings as a Service
Aug 13, 202018 views
Digital media companies typically collect rich data in the form,of sequences of online user activities. Such data is used in various applications, involving tasks ranging from click or conversion,prediction to recommendation or user segmentation. Nonetheless,,each application depends upon specialized feature engineering that,requires a lot of effort and typically disregards the time-varying,nature of the online user behavior. Learning time-preserving vector representations of users (user embeddings), irrespective of a,specific task, would save redundant effort and potentially lead to,higher embedding quality. To that end, we address the limitations,of the current state-of-the-art self-supervised methods for taskindependent (unsupervised) sequence embedding, and propose a,novel Time-Aware Sequential Autoencoder (TASA) that accounts,for the temporal aspects of sequences of activities. The generated,embeddings are intended to be readily accessible for many problem formulations and seamlessly applicable to desired tasks, thus,sidestepping the burden of task-driven feature engineering. The proposed TASA shows improvements over alternative self-supervised,models in terms of sequence reconstruction. Moreover, the embeddings generated by TASA yield increases in predictive performance,on both proprietary and public data. It also achieves comparable,results to supervised approaches that are trained on individual,tasks separately and require substantially more computational effort. TASA has been incorporated within a pipeline designed to,provide,time-aware user embeddings as a service,, and the use of its,embeddings exhibited lifts in conversion prediction AUC on four,audiences.