# Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction

arXiv: https://arxiv.org/abs/1905.11900 · HTML: https://mlsanity.com/paper/1905.11900/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 14 of 14)

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- [Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks](https://mlsanity.com/paper/2301.08144/) (2023)
- [Learn Layer-wise Connections in Graph Neural Networks](https://mlsanity.com/paper/2112.13585/) (2021)
- [User-Side Realization](https://mlsanity.com/paper/2403.15757/) (2024)
- [TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation](https://mlsanity.com/paper/2101.05611/) (2021)
- [Overhead-free User-side Recommender Systems](https://mlsanity.com/paper/2411.07589/) (2024)
- [Application of Liquid Rank Reputation System for Content Recommendation](https://mlsanity.com/paper/2209.07641/) (2022)
- [IGNiteR: News Recommendation in Microblogging Applications (Extended Version)](https://mlsanity.com/paper/2210.01942/) (2022)
- [Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin](https://mlsanity.com/paper/2406.17904/) (2024)
- [AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side](https://mlsanity.com/paper/2609.31166/) (2026)

## Abstract (arXiv)

An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative Attention Network (SEAN) for content recommendation. SEAN uses a personalized content recommendation model to encourage personal interests driven recommendation. Moreover, SEAN allows the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets from a popular decentralized content distribution platform, Steemit, we compare SEAN with state-of-the-art CF and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation performance.