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

Abstract

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.

arXiv comment10 pages, accepted an oral paper in sigKDD2018(industry track)

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Cited by in corpus (9)

• CoLES: Contrastive Learning for Event Sequences with Self-Supervision
• Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
• Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
• EdgeRec: Recommender System on Edge in Mobile Taobao
• Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
• Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
• Denoising User-aware Memory Network for Recommendation
• AIBench Scenario: Scenario-distilling AI Benchmarking
• AIBench Training: Balanced Industry-Standard AI Training Benchmarking