UserBERT: Self-supervised User Representation Learning
This paper extends the BERT model to user data for pretraining user representations in a self-supervised way. By viewing actions (e.g., purchases and clicks) in behavior sequences (i.e., usage history) in an analogous way to words in sentences, we propose methods for the tokenization, the generation of input representation vectors and a novel pretext task to enable the pretraining model to learn from its own input, omitting the burden of collecting additional data. Further, our model adopts a unified structure to simultaneously learn from long-term and short-term user behavior as well as user profiles. Extensive experiments demonstrate that the learned representations result in significant improvements when transferred to three different real-world tasks, particularly in comparison with task-specific modeling and representations obtained from multi-task learning.
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