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.

翻译：用户代表制对于在工业中提供高质量的商业服务至关重要。通用用户代表制最近获得许多利益,我们可以摆脱为每个下游应用培训一个具体模式的繁琐工作。在本文件中,我们试图从两个观点改进通用用户代表制。首先,提出了一种截然不同的自我监督学习模式,以指导代表性模式培训。它提供了一个统一框架,允许以数据驱动的方式进行长期或短期代表制学习。此外,还提出了一个新的多种利益提取模块。模块引入了一种利益词典,以捕捉特定用户的主要利益,然后通过行为汇总产生他/她以利益为导向的表述。实验结果显示了学习的用户代表制的有效性和适用性。