Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling. (arXiv:2110.11337v1 [cs.LG])

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

Source: https://arxiv.org/abs/2110.11337


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