# Incremental user embedding modeling for personalized text classification

arXiv: https://arxiv.org/abs/2202.06369 · HTML: https://mlsanity.com/paper/2202.06369/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [Federated User Representation Learning](https://mlsanity.com/paper/1909.12535/) (2019)
- [Incremental Embedding Learning via Zero-Shot Translation](https://mlsanity.com/paper/2012.15497/) (2020)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [Consistent Dialogue Generation with Self-supervised Feature Learning](https://mlsanity.com/paper/1903.05759/) (2019)
- [Learning User Embeddings from Temporal Social Media Data: A Survey](https://mlsanity.com/paper/2105.07996/) (2021)

## Built on by (most cited first, 3 of 3)

- [Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement](https://mlsanity.com/paper/2402.11060/) (2024)
- [PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer](https://mlsanity.com/paper/2306.08126/) (2023)
- [Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs](https://mlsanity.com/paper/2606.23387/) (2026)

## Abstract (arXiv)

Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning by utilizing user personalized information has become increasingly challenging due to ever-growing history data. In this work, we propose an incremental user embedding modeling approach, in which embeddings of user's recent interaction histories are dynamically integrated into the accumulated history vectors via a transformer encoder. This modeling paradigm allows us to create generalized user representations in a consecutive manner and also alleviate the challenges of data management. We demonstrate the effectiveness of this approach by applying it to a personalized multi-class classification task based on the Reddit dataset, and achieve 9% and 30% relative improvement on prediction accuracy over a baseline system for two experiment settings through appropriate comment history encoding and task modeling.