# Enabling the Analysis of Personality Aspects in Recommender Systems

arXiv: https://arxiv.org/abs/2001.04825 · HTML: https://mlsanity.com/paper/2001.04825/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [A Survey on Personality-Aware Recommendation Systems](https://mlsanity.com/paper/2101.12153/) (2021)
- [A Query Language for Summarizing and Analyzing Business Process Data](https://mlsanity.com/paper/2105.10911/) (2021)
- [Intent Recognition in Conversational Recommender Systems](https://mlsanity.com/paper/2212.03721/) (2022)
- [Towards Artificial Intelligence Enabled Financial Crime Detection](https://mlsanity.com/paper/2105.10866/) (2021)
- [Using Machine Learning Based Models for Personality Recognition](https://mlsanity.com/paper/2201.06248/) (2022)
- [Towards Dynamic Feature Selection with Attention to Assist Banking Customers in Establishing a New Business](https://mlsanity.com/paper/2105.03852/) (2021)
- [An Intelligent Hybrid Model for Identity Document Classification](https://mlsanity.com/paper/2106.04345/) (2021)
- [Towards Knowledge-based Mining of Mental Disorder Patterns from Textual Data](https://mlsanity.com/paper/2207.06254/) (2022)
- [Towards Mining Creative Thinking Patterns from Educational Data](https://mlsanity.com/paper/2210.06118/) (2022)
- [TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation](https://mlsanity.com/paper/2607.25471/) (2026)

## Abstract (arXiv)

Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.