mlsanityData to 7 Oct 2026

A Survey on Personality-Aware Recommendation Systems

arXiv 2101.12153 ↗First posted 28 Jan 2021cs.IR cs.AI cs.CYcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 10 of its 200 references that are in the map. First posted on arXiv 28 Jan 2021.

Builds on

10 of 200 references.

References · 10

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2019Recent Trends in Deep Learning Based Personality Detectioncited by 29
  3. 2018PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationshipcited by 15
  4. 2018First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysiscited by 14
  5. 2020Enabling the Analysis of Personality Aspects in Recommender Systemscited by 10
  6. 2017Predicting Personality from Book Preferences with User-Generated Content Labelscited by 2
  7. 2020A Review of Personality in Human Robot Interactionscited by 2
  8. 2020Socially-Aware Conference Participant Recommendation with Personality Traitscited by 2
  9. 2016Towards Personality-Aware Recommendationcited by 1
  10. 2020A Recommender System based on the analysis of personality traits in Telegram social networkcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 5; 2025: 3; 2026: 4.

18 papers.

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  1. 2023Recent Developments in Recommender Systems: A Surveycited by 14
  2. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  3. 2022Personality-Driven Social Multimedia Content Recommendationcited by 6
  4. 2024Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectivescited by 6
  5. 2022Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computingcited by 5
  6. 2023Entity Aware Modelling: A Surveycited by 4
  7. 2024Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detectioncited by 3
  8. 2024Developing and Evaluating a Design Method for Positive Artificial Intelligencecited by 2
  9. 2025Twenty Years of Personality Computing: Threats, Challenges and Future Directionscited by 2
  10. 2024Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Modelcited by 1
  11. 2024Cross-domain Transfer of Valence Preferences via a Meta-optimization Approachcited by 1
  12. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
6 more
  1. 2021Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processingcited by 0
  2. 2023Recommender Systems: A Primercited by 0
  3. 2025A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendationcited by 0
  4. 2026FlexRec: Adapting LLM-based Recommenders for Flexible Needs via Reinforcement Learningcited by 0
  5. 2026Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detectioncited by 0
  6. 2026Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognitioncited by 0