# Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

arXiv: https://arxiv.org/abs/2102.09635 · HTML: https://mlsanity.com/paper/2102.09635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Minimizing Polarization and Disagreement in Social Networks](https://mlsanity.com/paper/1712.09948/) (2017)
- [Adversarial Learning for Debiasing Knowledge Graph Embeddings](https://mlsanity.com/paper/2006.16309/) (2020)
- [Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum](https://mlsanity.com/paper/1606.06356/) (2016)
- [Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality](https://mlsanity.com/paper/1812.11422/) (2018)
- [Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter](https://mlsanity.com/paper/1711.10251/) (2017)
- [Maximizing the Diversity of Exposure in a Social Network](https://mlsanity.com/paper/1809.04393/) (2018)

## Built on by (most cited first, 1 of 1)

- [Reducing Exposure to Harmful Content via Graph Rewiring](https://mlsanity.com/paper/2306.07930/) (2023)

## Abstract (arXiv)

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on random walks with erasure is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.