# Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation

arXiv: https://arxiv.org/abs/2207.14218 · HTML: https://mlsanity.com/paper/2207.14218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 28 Jul 2022.

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

- [Knowledge-aware Coupled Graph Neural Network for Social Recommendation](https://mlsanity.com/paper/2110.03987/) (2021)
- [Graph Embedding for Recommendation against Attribute Inference Attacks](https://mlsanity.com/paper/2101.12549/) (2021)
- [Metadata Embeddings for User and Item Cold-start Recommendations](https://mlsanity.com/paper/1507.08439/) (2015)
- [Research Commentary on Recommendations with Side Information: A Survey and Research Directions](https://mlsanity.com/paper/1909.12807/) (2019)
- [Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning](https://mlsanity.com/paper/1911.09872/) (2019)
- [Graph Convolution Machine for Context-aware Recommender System](https://mlsanity.com/paper/2001.11402/) (2020)
- [A Collective Variational Autoencoder for Top-N Recommendation with Side Information](https://mlsanity.com/paper/1807.05730/) (2018)
- [One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems](https://mlsanity.com/paper/1907.00119/) (2019)
- [Attribute-aware Diversification for Sequential Recommendations](https://mlsanity.com/paper/2008.00783/) (2020)

## Abstract (arXiv)

This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a conventional context-aware recommender system that leverages side information, we show that user attributes do not always improve recommendation. Then, we demonstrate that user attributes can negatively impact diversity and coverage. Finally, we investigate the amount of information about users that ``survives'' from the training data into the recommendation lists produced by the recommender. This information is a weak signal that could in the future be exploited for calibration or studied further as a privacy leak.