  Click-Through Rate (CTR) prediction on cold users is a challenging task in
recommender systems. Recent researches have resorted to meta-learning to tackle
the cold-user challenge, which either perform few-shot user representation
learning or adopt optimization-based meta-learning. However, existing methods
suffer from information loss or inefficient optimization process, and they fail
to explicitly model global user preference knowledge which is crucial to
complement the sparse and insufficient preference information of cold users. In
this paper, we propose a novel and efficient approach named RESUS, which
decouples the learning of global preference knowledge contributed by collective
users from the learning of residual preferences for individual users.
Specifically, we employ a shared predictor to infer basis user preferences,
which acquires global preference knowledge from the interactions of different
users. Meanwhile, we develop two efficient algorithms based on the nearest
neighbor and ridge regression predictors, which infer residual user preferences
via learning quickly from a few user-specific interactions. Extensive
experiments on three public datasets demonstrate that our RESUS approach is
efficient and effective in improving CTR prediction accuracy on cold users,
compared with various state-of-the-art methods.
