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Springer International Publishing
Reference33 articles.
1. Abdollahpouri, H., et al.: Multistakeholder recommendation: survey and research directions. User Model. User-Adap. Inter. 30(1), 127–158 (2020). https://doi.org/10.1007/s11257-019-09256-1
2. Abdollahpouri, H., Burke, R., Mansoury, M.: Unfair exposure of artists in music recommendation. CoRR (2020). https://arxiv.org/abs/2003.11634
3. Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, pp. 42–46. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3109859.3109912
4. Lecture Notes in Computer Science;I Adaji,2020
5. Adaji, I., Kiron, N., Vassileva, J.: Level of involvement and the influence of persuasive strategies in e-commerce: a game-based approach. In: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, pp. 325–332 (2021)
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