  Precise user modeling is critical for online personalized recommendation
services. Generally, users' interests are diverse and are not limited to a
single aspect, which is particularly evident when their behaviors are observed
for a longer time. For example, a user may demonstrate interests in cats/dogs,
dancing and food \& delights when browsing short videos on Tik Tok; the same
user may show interests in real estate and women's wear in her web browsing
behaviors. Traditional models tend to encode a user's behaviors into a single
embedding vector, which do not have enough capacity to effectively capture her
diverse interests.
  This paper proposes a Sequential User Matrix (SUM) to accurately and
efficiently capture users' diverse interests. SUM models user behavior with a
multi-channel network, with each channel representing a different aspect of the
user's interests. User states in different channels are updated by an
\emph{erase-and-add} paradigm with interest- and instance-level attention. We
further propose a local proximity debuff component and a highway connection
component to make the model more robust and accurate. SUM can be maintained and
updated incrementally, making it feasible to be deployed for large-scale online
serving. We conduct extensive experiments on two datasets. Results demonstrate
that SUM consistently outperforms state-of-the-art baselines.
