Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling
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Universal user representation is an important research topic in industry, and is widely used in diverse downstream user analysis tasks, such as user profiling and user preference prediction. With the rapid development of Internet service platforms, extremely long user behavior sequences have been accumulated. However, existing researches have little ability to model universal user representation based on lifelong sequences of user behavior since registration. In this study, we propose a novel framework called Lifelong User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g.,105); (ii) Self-supervised Multi-anchor EncoderNetwork (SMEN) maps sequences of BoI features to multiple low-dimensional user representations by contrastive learning. SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art unsupervised representation methods in downstream tasks
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