# Personality-Driven Social Multimedia Content Recommendation

arXiv: https://arxiv.org/abs/2207.12236 · HTML: https://mlsanity.com/paper/2207.12236/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 90 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 19 of 19)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Learning Factorized Multimodal Representations](https://mlsanity.com/paper/1806.06176/) (2018)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [A Survey on Personality-Aware Recommendation Systems](https://mlsanity.com/paper/2101.12153/) (2021)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship](https://mlsanity.com/paper/1811.08657/) (2018)
- [Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks](https://mlsanity.com/paper/2008.06716/) (2020)
- [25 Tweets to Know You: A New Model to Predict Personality with Social Media](https://mlsanity.com/paper/1704.05513/) (2017)
- [Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms](https://mlsanity.com/paper/1911.00936/) (2019)
- [SoMin.ai: Personality-Driven Content Generation Platform](https://mlsanity.com/paper/2011.14615/) (2020)
- [I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance](https://mlsanity.com/paper/2002.01726/) (2020)
- [Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling](https://mlsanity.com/paper/2106.10673/) (2021)

## Built on by (most cited first, 6 of 6)

- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation](https://mlsanity.com/paper/2602.16990/) (2026)
- [Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines](https://mlsanity.com/paper/2304.02313/) (2023)
- [Personality Analysis from Online Short Video Platforms with Multi-domain Adaptation](https://mlsanity.com/paper/2411.00813/) (2024)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [MindFuse: Towards GenAI Explainability in Marketing Strategy Co-Creation](https://mlsanity.com/paper/2512.04112/) (2025)

## Abstract (arXiv)

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations, which when deployed are able to improve digital advertising efficiency by over 420% as compared to the original human-guided approach.