A Multi-Stakeholder Recommender System for Rewards Recommendations-Reference-Cited by-同舟云学术

A Multi-Stakeholder Recommender System for Rewards Recommendations

Published:2022-09-18 Issue: Volume: Page:
ISSN:
Container-title:Sixteenth ACM Conference on Recommender Systems
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Author:

Ranjbar Kermany Naime¹,Pizzato Luiz¹,Min Thireindar¹,Scott Callum¹,Leontjeva Anna¹

Affiliation:

1. AI Labs, Commonwealth Bank of Australia, Australia

Publisher

ACM

Reference13 articles.

1. Himan Abdollahpouri Gediminas Adomavicius Robin Burke Ido Guy Dietmar Jannach Toshihiro Kamishima Jan Krasnodebski and Luiz Pizzato. 2019. Beyond personalization: Research directions in multistakeholder recommendation. arXiv preprint arXiv:1905.01986(2019). Himan Abdollahpouri Gediminas Adomavicius Robin Burke Ido Guy Dietmar Jannach Toshihiro Kamishima Jan Krasnodebski and Luiz Pizzato. 2019. Beyond personalization: Research directions in multistakeholder recommendation. arXiv preprint arXiv:1905.01986(2019).

2. Multistakeholder recommendation: Survey and research directions

3. A Conceptual Framework on Reconceptualizing Customer Share of Wallet (SOW): As a Perspective of Dynamic Process in the Hospitality Consumption Context

4. Jan Roelf Bult and Tom Wansbeek. 1995. Optimal selection for direct mail. In Marketing Science Vol. 14.4. 378–394. Jan Roelf Bult and Tom Wansbeek. 1995. Optimal selection for direct mail. In Marketing Science Vol. 14.4. 378–394.

5. Peter Fader. 2020. Customer centricity: Focus on the right customers for strategic advantage. Wharton digital press. Peter Fader. 2020. Customer centricity: Focus on the right customers for strategic advantage. Wharton digital press.

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