Papers using LensKit#

This page lists known papers using the Python version of LensKit. If you use LensKit for research, please e-mail Michael Ekstrand <ekstrand@acm.org> with a copy of your paper and bibliographic information so we can add it to this list.

generated by bibbase.org
  2025 (11)
User and Recommender Behavior Over Time: Contextualizing Activity Effectiveness Diversity and Fairness in Book Recommendation. Vaez Barenji, S.; Parajuli, S.; and Ekstrand, M. D. In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, of UMAP Adjunct '25, pages 280–287, New York, NY, USA, June 2025. Association for Computing Machinery
User and Recommender Behavior Over Time: Contextualizing Activity Effectiveness Diversity and Fairness in Book Recommendation [link]Paper   doi   link   bibtex   abstract  
Circumventing Misinformation Controls: Assessing the Robustness of Intervention Strategies in Recommender Systems. Pathak, R.; and Spezzano, F. In Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, of UMAP '25, pages 279–284, New York, NY, USA, June 2025. Association for Computing Machinery
Circumventing Misinformation Controls: Assessing the Robustness of Intervention Strategies in Recommender Systems [link]Paper   doi   link   bibtex   abstract  
Privacy Preservation through Practical Machine Unlearning. Dilworth, R. February 2025. arXiv:2502.10635 [cs]
Privacy Preservation through Practical Machine Unlearning [link]Paper   doi   link   bibtex   abstract  
Using emotion diversification based on movie reviews to improve the user experience of movie recommender systems. Lansman, L. Ph.D. Thesis, 2025. ISBN: 9798311930970 Pages: 83
Using emotion diversification based on movie reviews to improve the user experience of movie recommender systems [link]Paper   link   bibtex   abstract  
DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems. Mancino, A. C. M.; Bufi, S.; Fazio, A. D.; Ferrara, A.; Malitesta, D.; Pomo, C.; and Noia, T. D. April 2025. arXiv:2410.22972 [cs] version: 2
DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems [link]Paper   doi   link   bibtex   abstract  
Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling. Arabzadeh, A. Master's thesis, University of Siegen, February 2025. arXiv:2502.08845 [cs]
Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling [link]Paper   link   bibtex   abstract  
A Comparative Evaluation of Recommender Systems Tools. Akhadam, A.; Kbibchi, O.; Mekouar, L.; and Iraqi, Y. IEEE Access, 13: 29493–29522. 2025.
A Comparative Evaluation of Recommender Systems Tools [link]Paper   doi   link   bibtex   abstract  
Extending MovieLens-32M to Provide New Evaluation Objectives. Smucker, M. D.; and Chamani, H. April 2025. arXiv:2504.01863 [cs]
Extending MovieLens-32M to Provide New Evaluation Objectives [link]Paper   doi   link   bibtex   abstract  
Recall, Robustness, and Lexicographic Evaluation. Diaz, F.; Ekstrand, M. D.; and Mitra, B. ACM Trans. Recomm. Syst.. April 2025. Just Accepted
Recall, Robustness, and Lexicographic Evaluation [link]Paper   doi   link   bibtex   abstract  
  2024 (27)
Um estudo sobre bibliotecas para sistemas de recomendação em Python. Danesi, L. D. C. Ph.D. Thesis, Universidade Federal de Santa Maria, December 2024. Accepted: 2025-01-28T16:09:12Z Publisher: Universidade Federal de Santa Maria
Um estudo sobre bibliotecas para sistemas de recomendação em Python [link]Paper   link   bibtex   abstract  
Recommendations with minimum exposure guarantees: a post-processing framework. Lopes, R.; Alves, R.; Ledent, A.; Santos, R. L. T.; and Kloft, M. Expert Systems with Applications, 236: 121164. February 2024.
Recommendations with minimum exposure guarantees: a post-processing framework [link]Paper   doi   link   bibtex   abstract  
A Test Collection for Offline Evaluation of Recommender Systems. Chamani, H. . November 2024. Publisher: University of Waterloo
A Test Collection for Offline Evaluation of Recommender Systems [link]Paper   link   bibtex   abstract  
e-Fold Cross-Validation for Recommender-System Evaluation. Baumgart, M.; Wegmeth, L.; Vente, T.; and Beel, J. In First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood), October 2024.
e-Fold Cross-Validation for Recommender-System Evaluation [pdf]Paper   link   bibtex   abstract  
Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity. Pathak, R. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, of CIKM '24, pages 5471–5474, New York, NY, USA, October 2024. Association for Computing Machinery
Advancing Misinformation Awareness in Recommender Systems for Social Media Information Integrity [link]Paper   doi   link   bibtex   abstract  
Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance. Arabzadeh, A.; Vente, T.; and Beel, J. October 2024. Presented at International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood)
Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance [link]Paper   doi   link   bibtex   abstract  
Aprimorando a instalação e a configuração de experimentos do RecSysExp. Silva, S. C. d. Technical Report Universidade Federal de Ouro Preto, Ouro Preto, BR, 2024. Accepted: 2024-02-29T14:36:20Z
Aprimorando a instalação e a configuração de experimentos do RecSysExp. [link]Paper   link   bibtex   abstract  
Active learning in recommender systems for predicting vulnerabilities in software. Stijger, E. Master's thesis, Utrecht University, Utrecht, NL, 2024. Accepted: 2024-01-06T00:01:00Z
Active learning in recommender systems for predicting vulnerabilities in software [link]Paper   link   bibtex   abstract  
The Potential of AutoML for Recommender Systems. Vente, T.; and Beel, J. February 2024. arXiv:2402.04453 [cs]
The Potential of AutoML for Recommender Systems [link]Paper   doi   link   bibtex   abstract  
Recommender systems algorithm selection for ranking prediction on implicit feedback datasets. Wegmeth, L.; Vente, T.; and Beel, J. In RecSys '24 Late-Breaking Results, September 2024. arXiv:2409.05461 [cs]
Recommender systems algorithm selection for ranking prediction on implicit feedback datasets [link]Paper   doi   link   bibtex   abstract  
Methodologies to evaluate recommender systems. Michiels, L. Ph.D. Thesis, University of Antwerp, Antwerp, 2024.
Methodologies to evaluate recommender systems [link]Paper   doi   link   bibtex   abstract  
It's not you, it's me: the impact of choice models and ranking strategies on gender imbalance in music recommendation. Ferraro, A.; Ekstrand, M. D.; and Bauer, C. In Proceedings of the 18th ACM Conference on Recommender Systems, August 2024. ACM
It's not you, it's me: the impact of choice models and ranking strategies on gender imbalance in music recommendation [link]Paper   doi   link   bibtex   abstract  
Towards optimizing ranking in grid-layout for provider-side fairness. Raj, A.; and Ekstrand, M. D. In Proceedings of the 46th European Conference on Information Retrieval, volume 14612, of LNCS, pages 90–105, March 2024. Springer
Towards optimizing ranking in grid-layout for provider-side fairness [link]Paper   doi   link   bibtex   abstract  
Distributionally-informed recommender system evaluation. Ekstrand, M. D.; Carterette, B.; and Diaz, F. ACM Transactions on Recommender Systems, 2(1): 6:1–27. March 2024.
Distributionally-informed recommender system evaluation [link]Paper   doi   link   bibtex   abstract  
From Clicks to Carbon: The Environmental Toll of Recommender Systems. Vente, T.; Wegmeth, L.; Said, A.; and Beel, J. In Proceedings of the 18th ACM Conference on Recommender Systems, October 2024. ACM arXiv:2408.08203 [cs]
From Clicks to Carbon: The Environmental Toll of Recommender Systems [link]Paper   doi   link   bibtex   abstract  
Large Language Models as Recommender Systems: A Study of Popularity Bias. Lichtenberg, J. M.; Buchholz, A.; and Schwöbel, P. June 2024. arXiv:2406.01285 [cs]
Large Language Models as Recommender Systems: A Study of Popularity Bias [link]Paper   link   bibtex   abstract  
Towards Purpose-aware Privacy-Preserving Techniques for Predictive Applications. Slokom, M. Ph.D. Thesis, TU Delft, 2024.
Towards Purpose-aware Privacy-Preserving Techniques for Predictive Applications [link]Paper   link   bibtex   abstract  
The Impact of Cluster Centroid and Text Review Embeddings on Recommendation Methods. Dolog, P.; Sadikaj, Y.; Velaj, Y.; Stephan, A.; Roth, B.; and Plant, C. In Companion Proceedings of the ACM on Web Conference 2024, of WWW '24, pages 589–592, New York, NY, USA, May 2024. Association for Computing Machinery
The Impact of Cluster Centroid and Text Review Embeddings on Recommendation Methods [link]Paper   doi   link   bibtex   abstract  
Rethinking Recommender Systems: Cluster-based Algorithm Selection. Lizenberger, A.; Pfeifer, F.; and Polewka, B. May 2024. arXiv:2405.18011 [cs]
Rethinking Recommender Systems: Cluster-based Algorithm Selection [link]Paper   doi   link   bibtex   abstract  
Anonymity-Aware Framework for Designing Recommender Systems. Honda, M.; and Nishi, H. IEEJ Transactions on Electrical and Electronic Engineering, 19(9): 1455–1464. 2024. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.24093
Anonymity-Aware Framework for Designing Recommender Systems [link]Paper   doi   link   bibtex   abstract  
Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social Media. Pathak, R.; and Spezzano, F. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, of UMAP Adjunct '24, pages 80–85, New York, NY, USA, June 2024. Association for Computing Machinery
Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social Media [link]Paper   doi   link   bibtex   abstract  
Missing Data, Speculative Reading. Koeser, R. S.; and LeBlanc, Z. Journal of Cultural Analytics, 9(2). May 2024.
Missing Data, Speculative Reading [link]Paper   doi   link   bibtex   abstract  
Evaluating the performance-deviation of itemKNN in RecBole and LensKit. Schmidt, M.; Nitschke, J.; and Prinz, T. July 2024. arXiv:2407.13531 [cs]
Evaluating the performance-deviation of itemKNN in RecBole and LensKit [link]Paper   link   bibtex   abstract  
Multiple testing for IR and recommendation system experiments. Ihemelandu, N.; and Ekstrand, M. D. In Proceedings of the 46th European Conference on Information Retrieval, volume 14610, of LNCS, pages 449–457, March 2024. Springer
Multiple testing for IR and recommendation system experiments [link]Paper   doi   link   bibtex   abstract  
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems. Wegmeth, L.; Vente, T.; and Purucker, L. In Goharian, N.; Tonellotto, N.; He, Y.; Lipani, A.; McDonald, G.; Macdonald, C.; and Ounis, I., editor(s), Advances in Information Retrieval, pages 140–156, 2024. Springer Nature Switzerland
doi   link   bibtex   abstract  
An Empirical Analysis of Intervention Strategies’ Effectiveness for Countering Misinformation Amplification by Recommendation Algorithms. Pathak, R.; and Spezzano, F. In Goharian, N.; Tonellotto, N.; He, Y.; Lipani, A.; McDonald, G.; Macdonald, C.; and Ounis, I., editor(s), Advances in Information Retrieval, volume 14611, of LNCS, pages 285–301, 2024. Springer Nature Switzerland
doi   link   bibtex   abstract  
  2023 (6)
Candidate set sampling for evaluating top-N recommendation. Ihemelandu, N.; and Ekstrand, M. D. In Proceedings of the 22nd IEEE/WIC international conference on web intelligence and intelligent agent technology, pages 88–94, October 2023. arXiv:2309.11723 [cs]
Candidate set sampling for evaluating top-N recommendation [link]Paper   doi   link   bibtex   abstract  
Modeling uncertainty to improve personalized recommendations via Bayesian deep learning. Wang, X.; and Kadıoğlu, S. International Journal of Data Science and Analytics, 16(2): 191–201. August 2023.
Modeling uncertainty to improve personalized recommendations via Bayesian deep learning [link]Paper   doi   link   bibtex   abstract  
The effect of random seeds for data splitting on recommendation accuracy. Wegmeth, L.; Vente, T.; Purucker, L.; and Beel, J. In Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES 2023), September 2023.
link   bibtex   abstract  
Introducing LensKit-Auto, an experimental automated recommender system (AutoRecSys) toolkit. Vente, T.; Ekstrand, M.; and Beel, J. In Proceedings of the 17th ACM Conference on Recommender Systems, of RecSys '23, pages 1212–1216, New York, NY, USA, September 2023. Association for Computing Machinery
Introducing LensKit-Auto, an experimental automated recommender system (AutoRecSys) toolkit [link]Paper   doi   link   bibtex   abstract   1 download  
Mitigating mainstream bias in recommendation via cost-sensitive learning. Li, R. Z.; Urbano, J.; and Hanjalic, A. July 2023. arXiv:2307.13632 [cs]
Mitigating mainstream bias in recommendation via cost-sensitive learning [link]Paper   doi   link   bibtex   abstract  
Inference at scale: significance testing for large search and recommendation experiments. Ihemelandu, N.; and Ekstrand, M. D. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, of SIGIR '23, pages 2087–2091, New York, NY, USA, July 2023. Association for Computing Machinery
Inference at scale: significance testing for large search and recommendation experiments [link]Paper   doi   link   bibtex   abstract  
  2022 (1)
Measuring fairness in ranked results: an analytical and empirical comparison. Raj, A.; and Ekstrand, M. D In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 726–736, July 2022. ACM
Measuring fairness in ranked results: an analytical and empirical comparison [link]Paper   doi   link   bibtex   abstract   4 downloads  
  2021 (1)
Exploring author gender in book rating and recommendation. Ekstrand, M. D; and Kluver, D. User Modeling and User-Adapted Interaction, 31(3): 377–420. July 2021.
Exploring author gender in book rating and recommendation [link]Paper   doi   link   bibtex   abstract   12 downloads  
  2020 (4)
Music recommendation using genetic programming. Vanhaesebroeck, R. Master's thesis, Ghent University, Belgium, 2020.
Music recommendation using genetic programming [pdf]Paper   link   bibtex  
User-Specific Bicluster-Based Collaborative Filtering. da Silva, M. M. G. Master's thesis, Universidade de Lisboa (Portugal), Portugal, 2020. ISBN: 9798209925156
User-Specific Bicluster-Based Collaborative Filtering [link]Paper   link   bibtex   abstract  
Evaluating stochastic rankings with expected exposure. Diaz, F.; Mitra, B.; Ekstrand, M. D; Biega, A. J; and Carterette, B. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, of CIKM '20, October 2020. ACM
Evaluating stochastic rankings with expected exposure [link]Paper   doi   link   bibtex   abstract   1 download  
Comparing fair ranking metrics. Raj, A.; Wood, C.; Montoly, A.; and Ekstrand, M. D In September 2020.
Comparing fair ranking metrics [link]Paper   link   bibtex   abstract