- RecSys WorkshopUsing LLMs to Capture Users’ Temporal Context for RecommendationIn Workshop on Context-Aware Recommender Systems in conjunction with ACM RecSys 2025
- RecSysOpening the Black Box: Interpretable Remedies for Popularity Bias in Recommender SystemsIn Proceedings of the 19th ACM Conference on Recommender Systems, 2025
- RecSysMitigating Popularity Bias in Counterfactual Explanations using Large Language ModelsIn Proceedings of the 19th ACM Conference on Recommender Systems, 2025
- CIKMMitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsIn the 33rd ACM International Conference on Information and Knowledge Management, 2024
- RecSys WorkshopCorrecting for Popularity Bias in Recommender Systems via Item Loss EqualizationIn International Workshop on Recommender Systems for Sustainability and Social Good in conjunction with ACM RecSys 2024
- BNAICPotential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered AnalysisIn BNAIC Joint International Scientific Conference on AI and Machine Learning, 2023
- RecSys WorkshopFairness of Exposure in Dynamic RecommendationIn CONSEQUENCE Workshop on Causality, Counterfactuals, and Sequential Decision-Making in conjunction with ACM RecSys 2023
- RecSys WorkshopCareer Path Recommendations for Long-term Income Maximization: A Reinforcement Learning ApproachIn Workshop on Recommender Systems for Human Resources in conjunction with ACM RecSys 2023
- RecSys WorkshopExposure-Aware Recommendation using Contextual BanditsIn 5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022
- TOISA Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender SystemsACM Transactions on Information Systems, 40 no. 2, 2021
- UMAPUser-centered evaluation of popularity bias in recommender systemsIn Proceedings of the 29th ACM conference on user modeling, adaptation and personalization, 2021
- UMAPBeyond Algorithmic Fairness in Recommender SystemsIn Adjunct proceedings of the 29th ACM conference on user modeling, adaptation and personalization, 2021
- TISTFlatter is better: Percentile Transformations for Recommender SystemsACM Transactions on Intelligent Systems and Technology, 12 no. 2, 2021
- WSDMFairness-aware Recommendation in Multi-sided PlatformsIn Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
- RecSysThe Connection between Popularity Bias, Calibration, and Fairness in RecommendationIn the Proceedings of the 14th ACM Conference on Recommender Systems, 2020
- CIKMFeedback Loop and Bias Amplification in Recommender SystemsIn the 29th ACM International Conference on Information and Knowledge Management, 2020
- KDD WorkshopMulti-sided Exposure Bias in RecommendationIn Workshop on Industrial Recommendation Systems in Conjuction with ACM KDD, 2020
- UMAPFairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender SystemsIn the 28th Conference on User Modeling, Adaptation and Personalization, 2020
- FLAIRSInvestigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender SystemsIn the 33nd International Florida Artificial Intelligence Research Society Conference in Cooperation with AAAI, 2020
Recent Publications
You can find the full list of my articles on my Google Scholar profile.