  Learning user representations is a vital technique toward effective user
modeling and personalized recommender systems. Existing approaches often derive
an individual set of model parameters for each task by training on separate
data. However, the representation of the same user potentially has some
commonalities, such as preference and personality, even in different tasks. As
such, these separately trained representations could be suboptimal in
performance as well as inefficient in terms of parameter sharing.
  In this paper, we delve on research to continually learn user representations
task by task, whereby new tasks are learned while using partial parameters from
old ones. A new problem arises since when new tasks are trained, previously
learned parameters are very likely to be modified, and as a result, an
artificial neural network (ANN)-based model may lose its capacity to serve for
well-trained previous tasks forever, this issue is termed catastrophic
forgetting. To address this issue, we present \emph{Conure} the first
\underline{con}tinual, or lifelong, \underline{u}ser \underline{re}presentation
learner -- i.e., learning new tasks over time without forgetting old ones.
Specifically, we propose iteratively removing less important weights of old
tasks in a deep user representation model, motivated by the fact that neural
network models are usually over-parameterized. In this way, we could learn many
tasks with a single model by reusing the important weights, and modifying the
less important weights to adapt to new tasks. We conduct extensive experiments
on two real-world datasets with nine tasks and show that \emph{Conure} largely
exceeds the standard model that does not purposely preserve such old
"knowledge", and performs competitively or sometimes better than models which
are trained either individually for each task or simultaneously by merging all
task data.
