Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning

Author:

Shrivastava Rahul,Singh Sisodia Dilip,Kumar Nagwani Naresh

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference78 articles.

1. Abdollahpouri, H. (2019). Incorporating system-level objectives into recommender systems. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 2, 2–6. 10.1145/3308560.3314201.

2. Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., & Pizzato, L. (2019). Beyond Personalization: Research Directions in Multistakeholder Recommendation. May. http://arxiv.org/abs/1905.01986.

3. Abdollahpouri, H., & Burke, R. (2019). Multi-stakeholder recommendation and its connection to multi-sided fairness. CEUR Workshop Proceedings, 2440.

4. Abdollahpouri, H., Burke, R., & Mobasher, B. (2017). Recommender systems as multistakeholder environments. UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 347–348. 10.1145/3079628.3079657.

5. Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2020). Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration. In arXiv (pp. 1–16).

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