  User representation is essential for providing high-quality commercial
services in industry. Universal user representation has received many interests
recently, with which we can be free from the cumbersome work of training a
specific model for each downstream application. In this paper, we attempt to
improve universal user representation from two points of views. First, a
contrastive self-supervised learning paradigm is presented to guide the
representation model training. It provides a unified framework that allows for
long-term or short-term interest representation learning in a data-driven
manner. Moreover, a novel multi-interest extraction module is presented. The
module introduces an interest dictionary to capture principal interests of the
given user, and then generate his/her interest-oriented representations via
behavior aggregation. Experimental results demonstrate the effectiveness and
applicability of the learned user representations.
