  Individual user profiles and interaction histories play a significant role in
providing customized experiences in real-world applications such as chatbots,
social media, retail, and education. Adaptive user representation learning by
utilizing user personalized information has become increasingly challenging due
to ever-growing history data. In this work, we propose an incremental user
embedding modeling approach, in which embeddings of user's recent interaction
histories are dynamically integrated into the accumulated history vectors via a
transformer encoder. This modeling paradigm allows us to create generalized
user representations in a consecutive manner and also alleviate the challenges
of data management. We demonstrate the effectiveness of this approach by
applying it to a personalized multi-class classification task based on the
Reddit dataset, and achieve 9% and 30% relative improvement on prediction
accuracy over a baseline system for two experiment settings through appropriate
comment history encoding and task modeling.
