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Tasks such as search and recommendation have become increas- ingly importantfor E-commerce to deal with the information over- load problem. To meet thediverse needs of di erent users, person- alization plays an important role. Inmany large portals such as Taobao and Amazon, there are a bunch of di erenttypes 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 acrossmultiple tasks for more e ective personalization. In partic- ular, userbehavior sequences (e.g., click, bookmark or purchase of products) are modeledby LSTM and attention mechanism by integrating all the corresponding content,behavior and temporal information. User representations are shared and learnedin an end-to-end setting across multiple tasks. Bene ting from betterinformation utilization of multiple tasks, the user representations are more eective to re ect their interests and are more general to be transferred to newtasks. We refer this work as Deep User Perception Network (DUPN) and conduct anextensive set of o ine and online experiments. Across all tested ve di erenttasks, our DUPN consistently achieves better results by giving more e ectiveuser representations. Moreover, we deploy DUPN in large scale operational tasksin Taobao. Detailed implementations, e.g., incre- mental model updating, arealso provided to address the practical issues for the real world applications.

Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks