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View all- Sombattheera C(2022)News Feed: A Multiagent-Based Push Notification SystemMulti-disciplinary Trends in Artificial Intelligence10.1007/978-3-031-20992-5_11(120-132)Online publication date: 10-Nov-2022
Generation and Evaluation of Personalised Push-Notifications
Pages 223 - 224
Abstract
A shared challenge in the domain of User Modeling, Adaptation and Personalisation is proposed for the 2019 EvalUMAP workshop whereby the evaluation of user models generating personalised push-notifications is to be explored. As such, this paper presents a description of the evaluation process, a solution to the first proposed challenge, a discussion of results obtained from the Gym-Push evaluation environment and a number of benchmarks which can be used as a baseline for future work.
References
[1]
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, andWojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540
[2]
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017. Improved training of wasserstein gans. In Advances in Neural Information Processing Systems. 5767--5777.
[3]
Marius Kaminskas and Derek Bridge. 2016. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst. 7, 1, Article 2 (Dec. 2016), 42 pages.
[4]
Liadh Kelly Bilal Yousuf Owen Conlan, Kieran Fraser. 2019. Proposal for a Shared Challenge in the UMAP Space. Retrieved March 20, 2019 from http://evalumap. adaptcentre.ie/whitePaper.pdf
[5]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.
Index Terms
- Generation and Evaluation of Personalised Push-Notifications
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Published In
June 2019
455 pages
ISBN:9781450367110
DOI:10.1145/3314183
- General Chairs:
- George Angelos Papadopoulos,
- George Samaras,
- Stephan Weibelzahl,
- Program Chairs:
- Dietmar Jannach,
- Olga C. Santos
Copyright © 2019 ACM.
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Publication History
Published: 06 June 2019
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- Short-paper
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Conference
UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization
June 9 - 12, 2019
Larnaca, Cyprus
Acceptance Rates
UMAP'19 Adjunct Paper Acceptance Rate 30 of 122 submissions, 25%;
Overall Acceptance Rate 162 of 633 submissions, 26%
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Cited By
View all- Sombattheera C(2022)News Feed: A Multiagent-Based Push Notification SystemMulti-disciplinary Trends in Artificial Intelligence10.1007/978-3-031-20992-5_11(120-132)Online publication date: 10-Nov-2022
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