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D. Zein, und C. da Costa Pereira. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 170-178. ACM, (Juli 2022)
R. Legaspi, W. Xu, T. Konishi, S. Wada, und Y. Ishikawa. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 34-47. ACM, (Juli 2022)
V. Robbemond, O. Inel, und U. Gadiraju. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 223-233. ACM, (Juli 2022)
M. Chatti, M. Guesmi, L. Vorgerd, T. Ngo, S. Joarder, Q. Ain, und A. Muslim. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 254-264. ACM, (Juli 2022)Several detail-levels of explanations and their match to individual differences.
P. Sanchez, und L. Dietz. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 132-142. ACM, (Juli 2022)Assessing the value of RecSys you need to distinguish user types - and it can be done by clustering.
L. Steinert, F. Kölling, F. Putze, D. Küster, und T. Schultz. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 89-98. ACM, (Juli 2022)Example of a patient focused recsys and evaluation.
A. Majjodi, A. Starke, und C. Trattner. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 48-56. ACM, (Juli 2022)Controversial results on decreasing value of recommendation....
T. Kleemann, B. Loepp, und J. Ziegler. Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 74-79. ACM, (Juli 2022)
D. Zaken, A. Segal, D. Cavalier, G. Shani, und K. Gal. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 69-78. ACM, (Juli 2022)
N. Hazrati, und F. Ricci. Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 95-98. ACM, (Juli 2022)
G. Zhou, T. Umada, und S. D\textquotesingleMello. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 295-305. ACM, (2022)User tracing provide evidence of learning, but scalar parameters are better than sequences.