The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs).
It recently came to my attention that I was waging a war across multiple fronts and fatigue had struck — they were winning. For months I had battled, fighting their persistence with my propensity to click x.
Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA. Abstract: In this talk, we will survey how Deep Learning methods can be ap…
S. Zhang, and K. Balog. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &$\mathsemicolon$ Data Mining, page 1512-1520. ACM, (August 2020)
A. Ajalloeian, M. Vlachos, J. Schneider, and A. Steinmann. Proceedings of the 31st ACM International Conference on Information &$\mathsemicolon$ Knowledge Management, page 2853-2862. ACM, (October 2022)Very interesting case of recommendations. Music to play... Need to have skill level and also same music could be recommended many times....
Y. Qin, P. Wang, B. Ma, and Z. Zhang. Proceedings of the 31st ACM International Conference on Information &$\mathsemicolon$ Knowledge Management, page 1655–1665. ACM, (October 2022)
N. Hazrati, and F. Ricci. Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, page 95-98. ACM, (July 2022)
S. Eden, A. Livne, O. Shalom, B. Shapira, and D. Jannach. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, page 99-109. ACM, (July 2022)
M. Chatti, M. Guesmi, L. Vorgerd, T. Ngo, S. Joarder, Q. Ain, and A. Muslim. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, page 254-264. ACM, (July 2022)Several detail-levels of explanations and their match to individual differences.
D. Zaken, A. Segal, D. Cavalier, G. Shani, and K. Gal. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, page 69-78. ACM, (July 2022)
L. Steinert, F. Kölling, F. Putze, D. Küster, and T. Schultz. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, page 89-98. ACM, (July 2022)Example of a patient focused recsys and evaluation.
M. Millecamp, N. Htun, Y. Jin, and K. Verbert. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, page 101-109. ACM, (July 2018)