Prof. Nava Tintarev
Zitiert von
Zitiert von
A survey of explanations in recommender systems
N Tintarev, J Masthoff
2007 IEEE 23rd international conference on data engineering workshop, 801-810, 2007
Designing and evaluating explanations for recommender systems
N Tintarev, J Masthoff
Recommender systems handbook, 479-510, 2011
Explaining recommendations: Design and evaluation
N Tintarev, J Masthoff
Recommender systems handbook, 353-382, 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
Effective explanations of recommendations: user-centered design
N Tintarev, J Masthoff
Proceedings of the 2007 ACM conference on Recommender systems, 153-156, 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
Explanations of recommendations
N Tintarev
Proceedings of the 2007 ACM conference on Recommender systems, 203-206, 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
Formal arguments, preferences, and natural language interfaces to humans: an empirical evaluation
F Cerutti, N Tintarev, N Oren
ECAI 2014, 207-212, 2014
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
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
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, ...
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
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
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
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
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
A Diversity Adjusting Strategy with Personality for Music Recommendation.
F Lu, N Tintarev
IntRS@ RecSys, 7-14, 2018
This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias
A Rieger, T Draws, M Theune, N Tintarev
🏆 Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 189-199, 2021
Recommender systems under European AI regulations
T Di Noia, N Tintarev, P Fatourou, M Schedl
Communications of the ACM 65 (4), 69-73, 2022
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20