Di Noia, T., Tintarev, N., Fatourou, P., & Schedl, M. (2022). Recommender Systems under European AI Regulations. Communications of the Acm, 65(4), 69-73. https://doi.org/10.1145/3512728

Tintarev, N., & Masthoff, J. (2022). Beyond Explaining Single Item Recommendations. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook (3 ed., pp. 711-756). Springer. https://doi.org/10.1007/978-1-0716-2197-4_19

Draws, T., Inel, O., Tintarev, N., Baden, C., & Timmermans, B. (2022). Comprehensive Viewpoint Representations for a Deeper Understanding of User Interactions With Debated Topics. In CHIIR '22: ACM SIGIR Conference on Human Information Interaction and Retrieval (pp. 135-145). Association for Computing Machinery (ACM). https://doi.org/10.1145/3498366.3505812

Bianchi, F., HIlls, S., Rossini, P., Hovy, D., Tromble, R., & Tintarev, N. (2022). "It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online. https://doi.org/10.48550/arXiv.2210.15870

Rieger, A., Shaheen, Q-U-A., Sierra, C., Theune, M., & Tintarev, N. (2022). Towards Healthy Engagement with Online Debates: An Investigation of Debate Summaries and Personalized Persuasive Suggestions. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 192-199) https://doi.org/10.1145/3511047.3537692

Tran, T. N. T., Felfernig, A., & Tintarev, N. (2021). Humanized Recommender Systems: State-of-the-art and Research Issues. ACM Transactions on Interactive Intelligent Systems, 11(2), [9]. https://doi.org/10.1145/3446906

Burke, R., Ekstrand, M. D., Tintarev, N., & Vassileva, J. (2021). Preface to the special issue on fair, accountable, and transparent recommender systems. User Modeling and User-Adapted Interaction, 31(3), 371-375. https://doi.org/10.1007/s11257-021-09297-5

Draws, T., Rieger, A., Inel, O., Gadiraju, U., & Tintarev, N. (2021). A Checklist to Combat Cognitive Biases in Crowdsourcing. In E. Kamar, & K. Luther (Eds.), Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing: hcomp-21 (Vol. 9, pp. 48-59). AAAI Press. https://doi.org/10.1609/hcomp.v9i1.18939

Reuver, M., Mattis, N., Sax, M., Verberne, S., Tintarev, N., Helberger, N., Moeller, J., Vrijenhoek, S., Fokkens, A., & van Atteveldt, W. (2021). Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content. In Proceedings of the 1ste Workshop on NLP for Positive Impact (August 2021 ed., pp. 47-59). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.nlp4posimpact-1.6

Draws, T., Tintarev, N., & Gadiraju, U. (2021). Assessing viewpoint diversity in search results using ranking fairness metrics. ACM SIGKDD Explorations Newsletter, 23(1), 50-58. https://doi.org/10.1145/3468507.3468515