> update readme.md

README.md

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 # Optimizing Neighborhoods for Fair Top-N Recommendation
 
-## Abstract
-This paper addresses demographic bias in *neighborhood-learning* models for collaborative filtering recommendations. Despite their superior ranking performance, these methods can learn neighborhoods that inadvertently foster discriminatory patterns. There is limited research in this area, highlighting an important research gap. A notable yet solitary effort, *Balanced Neighborhood Sparse Linear Method (BNSLIM)*, aims at balancing neighborhood influence across different demographic groups. However, BNSLIM is hampered by computational inefficiency, and its rigid balancing approach often impacts accuracy. In that vein, we introduce two novel algorithms.
+Official implementation of our UMAP 2024 paper: **Optimizing Neighborhoods for Fair Top-N Recommendation**.
 
-The first, an enhancement of BNSLIM, incorporates the *Alternating Direction Method of Multipliers (ADMM)* to optimize all similarities concurrently, greatly reducing training time.
-
-The second, *Fairly Sparse Linear Regression (FSLR)*, induces controlled sparsity in neighborhoods to reveal correlations among different demographic groups, achieving comparable efficiency while being more accurate.
-
-Their performance is evaluated using standard exposure metrics alongside a new metric for user coverage disparities.
-
-Our experiments cover various applications, including a novel exploration of bias in course recommendations by teachers' country development status.
-
-Our results show the effectiveness of our algorithms in imposing fairness compared to BNSLIM and other well-known fairness approaches.
+> **Abstract:** 
+>*This paper addresses demographic bias in neighborhood-learning models for collaborative filtering recommendations. Despite their superior ranking performance, these methods can learn neighborhoods that inadvertently foster discriminatory patterns. There is limited research in this area, highlighting an important research gap. A notable yet solitary effort, Balanced Neighborhood Sparse Linear Method (BNSLIM), aims at balancing neighborhood influence across different demographic groups. However, BNSLIM is hampered by computational inefficiency, and its rigid balancing approach often impacts accuracy. In that vein, we introduce two novel algorithms. The first, an enhancement of BNSLIM, incorporates the Alternating Direction Method of Multipliers (ADMM) to optimize all similarities concurrently, greatly reducing training time. The second, Fairly Sparse Linear Regression (FSLR), induces controlled sparsity in neighborhoods to reveal correlations among different demographic groups, achieving comparable efficiency while being more accurate. Their performance is evaluated using standard exposure metrics alongside a new metric for user coverage disparities. Our experiments cover various applications, including a novel exploration of bias in course recommendations by teachers' country development status. Our results show the effectiveness of our algorithms in imposing fairness compared to BNSLIM and other well-known fairness approaches.*
 
 ## Repository Content
 This repository contains all the code used for our experiments. The `src` folder includes helper functions, covering data processing functions and metrics, and all the models used in our experiments. Additionally, the `notebooks` folder contains a notebook for each experiment, along with a notebook that summarizes the outcomes of all experiments.
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 ```
 
 ## Acknowledgements
-If you utilize any part of this code for your research, please consider giving a star to this repository and citing our work:
+If you utilize any part of this code for your research, please consider giving a star ⭐ and citing our work:
 
 Stavroula Eleftherakis, Georgia Koutrika, and Sihem Amer-Yahia. 2024. *Optimizing Neighborhoods for Fair Top-N Recommendation*. In UMAP ’24: Proceedings of the 32nd International Conference on User Modeling, Adaptation, and Personalization, July 1–4, 2024, Cagliari, Sardinia, Italy.