# User Factor Adaptation for User Embedding via Multitask Learning

arXiv: https://arxiv.org/abs/2102.11103 · HTML: https://mlsanity.com/paper/2102.11103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [Modelling Context with User Embeddings for Sarcasm Detection in Social Media](https://mlsanity.com/paper/1607.00976/) (2016)
- [Multi-Task Learning for Mental Health using Social Media Text](https://mlsanity.com/paper/1712.03538/) (2017)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [Social Media-based User Embedding: A Literature Review](https://mlsanity.com/paper/1907.00725/) (2019)
- [Quantifying Mental Health from Social Media with Neural User Embeddings](https://mlsanity.com/paper/1705.00335/) (2017)
- [Improving Review Representations with User Attention and Product Attention for Sentiment Classification](https://mlsanity.com/paper/1801.07861/) (2018)
- [Using Noisy Self-Reports to Predict Twitter User Demographics](https://mlsanity.com/paper/2005.00635/) (2020)
- [Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users](https://mlsanity.com/paper/1411.0861/) (2014)
- [Joint Effects of Context and User History for Predicting Online Conversation Re-entries](https://mlsanity.com/paper/1906.01185/) (2019)
- [JNET: Learning User Representations via Joint Network Embedding and Topic Embedding](https://mlsanity.com/paper/1912.00465/) (2019)
- [Learning Representations of Social Media Users](https://mlsanity.com/paper/1812.00436/) (2018)

## Built on by (most cited first, 1 of 1)

- [Enriching Unsupervised User Embedding via Medical Concepts](https://mlsanity.com/paper/2203.10627/) (2022)

## Abstract (arXiv)

Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.