# Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

arXiv: https://arxiv.org/abs/2004.13715 · HTML: https://mlsanity.com/paper/2004.13715/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context](https://mlsanity.com/paper/2301.04248/) (2023)
- [Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives](https://mlsanity.com/paper/2402.09239/) (2024)
- [Choice-Aware User Engagement Modeling andOptimization on Social Media](https://mlsanity.com/paper/2104.00801/) (2021)
- [Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems](https://mlsanity.com/paper/2505.19441/) (2025)

## Abstract (arXiv)

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.