  Tasks such as search and recommendation have become increas- ingly important
for E-commerce to deal with the information over- load problem. To meet the
diverse needs of di erent users, person- alization plays an important role. In
many large portals such as Taobao and Amazon, there are a bunch of di erent
types of search and recommendation tasks operating simultaneously for person-
alization. However, most of current techniques address each task separately.
This is suboptimal as no information about users shared across di erent tasks.
In this work, we propose to learn universal user representations across
multiple tasks for more e ective personalization. In partic- ular, user
behavior sequences (e.g., click, bookmark or purchase of products) are modeled
by LSTM and attention mechanism by integrating all the corresponding content,
behavior and temporal information. User representations are shared and learned
in an end-to-end setting across multiple tasks. Bene ting from better
information utilization of multiple tasks, the user representations are more e
ective to re ect their interests and are more general to be transferred to new
tasks. We refer this work as Deep User Perception Network (DUPN) and conduct an
extensive set of o ine and online experiments. Across all tested ve di erent
tasks, our DUPN consistently achieves better results by giving more e ective
user representations. Moreover, we deploy DUPN in large scale operational tasks
in Taobao. Detailed implementations, e.g., incre- mental model updating, are
also provided to address the practical issues for the real world applications.
