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

arXiv: https://arxiv.org/abs/1805.10727 · HTML: https://mlsanity.com/paper/1805.10727/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 24 of 24)

- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [One4all User Representation for Recommender Systems in E-commerce](https://mlsanity.com/paper/2106.00573/) (2021)
- [KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](https://mlsanity.com/paper/2208.10174/) (2022)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [Task Relation-aware Continual User Representation Learning](https://mlsanity.com/paper/2306.01792/) (2023)
- [Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling](https://mlsanity.com/paper/2110.11337/) (2021)
- [Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling](https://mlsanity.com/paper/2106.07356/) (2021)
- [MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios](https://mlsanity.com/paper/2112.13753/) (2021)
- [Transferable and Forecastable User Targeting Foundation Model](https://mlsanity.com/paper/2412.12468/) (2024)
- [Interest-oriented Universal User Representation via Contrastive Learning](https://mlsanity.com/paper/2109.08865/) (2021)
- [MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://mlsanity.com/paper/2403.02607/) (2024)
- [Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2408.11372/) (2024)
- [UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing](https://mlsanity.com/paper/2307.09989/) (2023)
- [Improving conversion rate prediction via self-supervised pre-training in online advertising](https://mlsanity.com/paper/2401.16432/) (2024)
- [ESQA: Event Sequences Question Answering](https://mlsanity.com/paper/2407.12833/) (2024)
- [Dynamic Time-aware Continual User Representation Learning](https://mlsanity.com/paper/2504.16501/) (2025)
- [FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation](https://mlsanity.com/paper/2508.00956/) (2025)
- [Instruction-aware User Embedding via Synergistic Language and Representation Modeling](https://mlsanity.com/paper/2510.11016/) (2025)
- [SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents](https://mlsanity.com/paper/2605.14205/) (2026)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [UserBERT: Modeling Long- and Short-Term User Preferences via Self-Supervision](https://mlsanity.com/paper/2202.07605/) (2022)
- [Enhancing Recommendation with Denoising Auxiliary Task](https://mlsanity.com/paper/2409.17402/) (2024)
- [User Representation via Cross Multi-source Behavior Pre-training for Mobile Games](https://mlsanity.com/paper/2609.01057/) (2026)

## Abstract (arXiv)

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.