# Exploiting Behavioral Consistence for Universal User Representation

arXiv: https://arxiv.org/abs/2012.06146 · HTML: https://mlsanity.com/paper/2012.06146/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Scaling Law for Recommendation Models: Towards General-purpose User Representations](https://mlsanity.com/paper/2111.11294/) (2021)
- [One4all User Representation for Recommender Systems in E-commerce](https://mlsanity.com/paper/2106.00573/) (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)
- [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)
- [Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning](https://mlsanity.com/paper/2212.03760/) (2022)
- [AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking](https://mlsanity.com/paper/2310.09706/) (2023)
- [Task-Related Token Compression in Multimodal Large Language Models from an Explainability Perspective](https://mlsanity.com/paper/2506.01097/) (2025)
- [From Text to Context: An Entailment Approach for News Stakeholder Classification](https://mlsanity.com/paper/2405.08751/) (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)
- [Large-scale User Game Lifecycle Representation Learning](https://mlsanity.com/paper/2510.15412/) (2025)
- [FISSION: Label Augmentation for Bot Detection](https://mlsanity.com/paper/2609.26279/) (2026)

## Abstract (arXiv)

User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task as in previous works. In specific, we propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation. It includes two key components. The first one is a new learning objective, which guides the model to fully identify and preserve valuable user information under a self-supervised learning framework. The other one is a multi-hop aggregation layer, which benefits the model capacity in aggregating diverse behaviors. Extensive experiments on benchmark datasets show that our approach can outperform state-of-the-art unsupervised representation methods, and even compete with supervised ones.