| A survey of explanations in recommender systems N Tintarev, J Masthoff 2007 IEEE 23rd international conference on data engineering workshop, 801-810, 2007 | 787 | 2007 |
| Designing and evaluating explanations for recommender systems N Tintarev, J Masthoff Recommender systems handbook, 479-510, 2011 | 503 | 2011 |
| Explaining recommendations: Design and evaluation N Tintarev, J Masthoff Recommender systems handbook, 353-382, 2015 | 450 | 2015 |
| Evaluating the effectiveness of explanations for recommender systems: Methodological issues and empirical studies on the impact of personalization N Tintarev, J Masthoff User Modeling and User-Adapted Interaction 22, 399-439, 2012 | 425 | 2012 |
| Effective explanations of recommendations: user-centered design N Tintarev, J Masthoff Proceedings of the 2007 ACM conference on Recommender systems, 153-156, 2007 | 257 | 2007 |
| Rate it again: increasing recommendation accuracy by user re-rating X Amatriain, JM Pujol, N Tintarev, N Oliver Proceedings of the third ACM conference on Recommender systems, 173-180, 2009 | 230 | 2009 |
| Explanations of recommendations N Tintarev Proceedings of the 2007 ACM conference on Recommender systems, 203-206, 2007 | 190 | 2007 |
| Effects of personal characteristics on music recommender systems with different levels of controllability Y Jin, N Tintarev, K Verbert Proceedings of the 12th ACM Conference on Recommender Systems, 13-21, 2018 | 88 | 2018 |
| A checklist to combat cognitive biases in crowdsourcing T Draws, A Rieger, O Inel, U Gadiraju, N Tintarev 🏆 Proceedings of the AAAI Conference on Human Computation and Crowdsourcing …, 2021 | 86 | 2021 |
| Formal arguments, preferences, and natural language interfaces to humans: an empirical evaluation F Cerutti, N Tintarev, N Oren ECAI 2014, 207-212, 2014 | 76 | 2014 |
| SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments D Bountouridis, J Harambam, M Makhortykh, M Marrero, N Tintarev, ... | 71 | 2019 |
| Ensuring fairness in group recommendations by rank-sensitive balancing of relevance M Kaya, D Bridge, N Tintarev Proceedings of the 14th ACM Conference on recommender systems, 101-110, 2020 | 66 | 2020 |
| Adapting recommendation diversity to openness to experience: a study of human behaviour N Tintarev, M Dennis, J Masthoff International Conference on User Modeling, Adaptation, and Personalization …, 2013 | 66 | 2013 |
| Effects of personal characteristics in control-oriented user interfaces for music recommender systems Y Jin, N Tintarev, NN Htun, K Verbert User Modeling and User-Adapted Interaction 30 (2), 199-249, 2020 | 63 | 2020 |
| Recommender systems under European AI regulations T Di Noia, N Tintarev, P Fatourou, M Schedl Communications of the ACM 65 (4), 69-73, 2022 | 61 | 2022 |
| This is not what we ordered: Exploring why biased search result rankings affect user attitudes on debated topics T Draws, N Tintarev, U Gadiraju, A Bozzon, B Timmermans Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 59 | 2021 |
| Does reviewer recommendation help developers? V Kovalenko, N Tintarev, E Pasynkov, C Bird, A Bacchelli IEEE Transactions on Software Engineering 46 (7), 710-731, 2018 | 59 | 2018 |
| The effectiveness of personalized movie explanations: An experiment using commercial meta-data N Tintarev, J Masthoff Adaptive Hypermedia and Adaptive Web-Based Systems: 5th International …, 2008 | 57 | 2008 |
| A Diversity Adjusting Strategy with Personality for Music Recommendation. F Lu, N Tintarev IntRS@ RecSys, 7-14, 2018 | 55 | 2018 |
| Diversity in news recommendations A Bernstein, C De Vreese, N Helberger, W Schulz, K Zweig, C Baden, ... arXiv preprint arXiv:2005.09495, 2020 | 53 | 2020 |
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