Published in Machine Learning Journal (Impact Factor: 4.3), 2025
A framework that mitigates representational bias in dense neural ranking models while preserving utility.
Recommended citation: Seyedsalehi, S., Zihayat, M., & Bagheri, E. (2025). A Regularization Framework for Bias Mitigation in Dense Neural Rankers. Machine Learning Journal.
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Published in ACM Transactions on Intelligent Systems and Technology (Impact Factor: 6.6), 2025
A neural proximity-based model for predicting query performance using latent query representations.
Recommended citation: Bigdeli, A., Ebrahimi, S., Arabzadeh, N., Salamat, S., Seyedsalehi, S., Khodabakhsh, M., Zarinkalam, F., & Bagheri, E. (2025). Query Performance Prediction Using Neural Query Space Proximity. ACM Transactions on Intelligent Systems and Technology.
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Published in Foundations and Trends® in Information Retrieval (Impact Factor: 10.4), 2024
A systematic exploration of gender bias sources in IR pipelines and mitigation strategies across benchmarks.
Recommended citation: Seyedsalehi, S., Bigdeli, A., Arabzadeh, N., AlMousawi, B., Marshall, Z., Zihayat, M., & Bagheri, E. (2024). Understanding and Mitigating Gender Bias in Information Retrieval Systems. Foundations and Trends® in Information Retrieval.
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Published in Machine Learning Journal (Impact Factor: 4.3), 2024
A disentanglement-based neural ranking model that separates semantic relevance from gender information.
Recommended citation: Seyedsalehi, S., Salamat, S., Arabzadeh, N., Ebrahimi, S., Zihayat, M., & Bagheri, E. (2024). Gender Disentangled Representation Learning in Neural Rankers. Machine Learning Journal.
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Published in Machine Learning Journal (Impact Factor: 4.3), 2024
Contrastive disentanglement of query attributes for robust performance prediction in retrieval.
Recommended citation: Salamat, S., Arabzadeh, N., Seyedsalehi, S., Bigdeli, A., Zihayat, M., & Bagheri, E. (2024). A Contrastive Neural Disentanglement Approach for Query Performance Prediction. Machine Learning Journal.
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Published in AUT Journal of Modeling and Simulation, 2022
GAN-based modeling of wireless propagation channels for improved simulation fidelity.
Recommended citation: Seyedsalehi, S., Pourahmadi, V., Sheikhzadeh, H., & Gharari, A. H. (2022). Generative Adversarial Networks for Propagation Channel Modeling. AUT Journal of Modeling and Simulation.
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Published in WSDM 2025 (Core Rank: A), 2025
A self-paced learning framework that adaptively minimizes bias in neural ranking through loss-based sampling.
Recommended citation: Seyedsalehi, S., Le, H. S., Zihayat, M., & Bagheri, E. (2025). Self-Paced Fair Ranking with Loss as a Proxy for Bias. WSDM 2025 (Core Rank: A).
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Published in The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025, Core Rank: A*), 2025
Reinforcement learning techniques that enhance few-shot ranking tasks with minimal supervision.
Recommended citation: Soleimani, S., Ebrahimi, S., Seyedsalehi, S., Zarinkalam, F., & Bagheri, E. (2025). Reinforcement Learning for Effective Few-Shot Ranking. SIGIR 2025.
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Published in The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025, Core Rank: A*), 2025
A curriculum strategy that progressively reduces ranking bias in neural retrieval systems.
Recommended citation: Seyedsalehi, S., Le, H. S., Zihayat, M., & Bagheri, E. (2025). Bias-aware Curriculum Sampling for Fair Ranking. SIGIR 2025.
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Published in 45th European Conference on Information Retrieval (ECIR 2023, Core Rank: A), 2023
A learning-to-rank framework for persuasive argument retrieval from debate corpora.
Recommended citation: Salamat, S., Arabzadeh, N., Bigdeli, A., Seyedsalehi, S., Zihayat, M., & Bagheri, E. (2023). Don't Raise Your Voice, Improve Your Argument: Learning to Retrieve Convincing Arguments. ECIR 2023.
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Published in 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023, Core Rank: A), 2023
A model that isolates query difficulty from semantic intent for better ranking behavior analysis.
Recommended citation: Salamat, S., Arabzadeh, N., Seyedsalehi, S., Bigdeli, A., Zihayat, M., & Bagheri, E. (2023). Neural Disentanglement of Query Difficulty and Semantics. CIKM 2023.
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Published in 45th European Conference on Information Retrieval (ECIR 2023, Core Rank: A), 2023
A data-centric framework to correct annotator bias in relevance judgements for fair IR evaluation.
Recommended citation: Bigdeli, A., Arabzadeh, N., Seyedsalehi, S., Mitra, B., Zihayat, M., & Bagheri, E. (2023). De-Biasing Relevance Judgements for Fair Ranking. ECIR 2023.
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Published in The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022, Core Rank: A*), 2022
An empirical study addressing gender-based disparities in neural retrieval models.
Recommended citation: Seyedsalehi, S., Bigdeli, A., Arabzadeh, N., Zihayat, M., & Bagheri, E. (2022). Addressing Gender-related Performance Disparities in Neural Rankers. SIGIR 2022.
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Published in 25th International Conference on Extending Database Technology (EDBT 2022, Core Rank: A), 2022
A neural model that identifies cohesive collaboration teams in large-scale networks.
Recommended citation: Seyedsalehi, S., Hamidi Rad, R., Kargar, M., Zihayat, M., & Bagheri, E. (2022). A Neural Approach to Forming Coherent Teams in Collaboration Networks. EDBT 2022.
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Published in 25th International Conference on Extending Database Technology (EDBT 2022, Core Rank: A), 2022
A fairness-aware neural ranking framework to mitigate gender stereotypes in retrieval systems.
Recommended citation: Seyedsalehi, S., Bigdeli, A., Arabzadeh, N., Mitra, B., Zihayat, M., & Bagheri, E. (2022). Bias-aware Fair Neural Ranking for Addressing Stereotypical Gender Biases. EDBT 2022.
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Published in 44th European Conference on Information Retrieval (ECIR 2022, Core Rank: A), 2022
A computationally efficient bias mitigation approach for neural ranking models.
Recommended citation: Bigdeli, A., Arabzadeh, N., Seyedsalehi, S., Zihayat, M., & Bagheri, E. (2022). A Light-weight Strategy for Restraining Gender Biases in Neural Rankers. ECIR 2022.
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Published in The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021, Core Rank: A*), 2021
An empirical study on the relationship between bias and utility dimensions in ad hoc retrieval.
Recommended citation: Bigdeli, A., Arabzadeh, N., Seyedsalehi, S., Zihayat, M., & Bagheri, E. (2021). On the Orthogonality of Bias and Utility in Ad hoc Retrieval. SIGIR 2021.
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Published in 30th ACM International Conference on Information & Knowledge Management (CIKM 2021, Core Rank: A), 2021
A large-scale dataset and toolkit for supervised query reformulation in search systems.
Recommended citation: Arabzadeh, N., Bigdeli, A., Seyedsalehi, S., Zihayat, M., & Bagheri, E. (2021). Matches Made in Heaven: Toolkit and Large-Scale Datasets for Supervised Query Reformulation. CIKM 2021.
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