# A Survey on Personality-Aware Recommendation Systems

arXiv: https://arxiv.org/abs/2101.12153 · HTML: https://mlsanity.com/paper/2101.12153/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 200 references that are in the map.
- First posted on arXiv 28 Jan 2021.

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

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship](https://mlsanity.com/paper/1811.08657/) (2018)
- [First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis](https://mlsanity.com/paper/1804.08046/) (2018)
- [Enabling the Analysis of Personality Aspects in Recommender Systems](https://mlsanity.com/paper/2001.04825/) (2020)
- [Predicting Personality from Book Preferences with User-Generated Content Labels](https://mlsanity.com/paper/1707.06643/) (2017)
- [A Review of Personality in Human Robot Interactions](https://mlsanity.com/paper/2001.11777/) (2020)
- [Socially-Aware Conference Participant Recommendation with Personality Traits](https://mlsanity.com/paper/2008.04653/) (2020)
- [Towards Personality-Aware Recommendation](https://mlsanity.com/paper/1607.05088/) (2016)
- [A Recommender System based on the analysis of personality traits in Telegram social network](https://mlsanity.com/paper/2010.00643/) (2020)

## Built on by (most cited first, 18 of 18)

- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [Personality-Driven Social Multimedia Content Recommendation](https://mlsanity.com/paper/2207.12236/) (2022)
- [Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives](https://mlsanity.com/paper/2404.15067/) (2024)
- [Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing](https://mlsanity.com/paper/2205.02764/) (2022)
- [Entity Aware Modelling: A Survey](https://mlsanity.com/paper/2302.08406/) (2023)
- [Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection](https://mlsanity.com/paper/2408.08551/) (2024)
- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)
- [Twenty Years of Personality Computing: Threats, Challenges and Future Directions](https://mlsanity.com/paper/2503.02082/) (2025)
- [Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model](https://mlsanity.com/paper/2404.09045/) (2024)
- [Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach](https://mlsanity.com/paper/2406.16494/) (2024)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection](https://mlsanity.com/paper/2606.24985/) (2026)
- [Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing](https://mlsanity.com/paper/2105.11798/) (2021)
- [Recommender Systems: A Primer](https://mlsanity.com/paper/2302.02579/) (2023)
- [A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2506.11563/) (2025)
- [FlexRec: Adapting LLM-based Recommenders for Flexible Needs via Reinforcement Learning](https://mlsanity.com/paper/2603.11901/) (2026)
- [Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition](https://mlsanity.com/paper/2607.08374/) (2026)

## Abstract (arXiv)

With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.