Calibrated Recommendations as a Minimum-Cost Flow Problem-Reference-Cited by-同舟云学术

Calibrated Recommendations as a Minimum-Cost Flow Problem

Published:2023-02-27 Issue: Volume: Page:
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Container-title:Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
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Author:

Abdollahpouri Himan¹^ORCID,Nazari Zahra¹^ORCID,Gain Alex¹^ORCID,Gibson Clay²^ORCID,Dimakopoulou Maria¹^ORCID,Anderton Jesse¹^ORCID,Carterette Benjamin¹^ORCID,Lalmas Mounia³^ORCID,Jebara Tony¹^ORCID

Affiliation:

1. Spotify, New York, NY, USA

2. Airbnb, San Francisco, CA, USA

3. Spotify, London, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539597.3570402

Reference30 articles.

1. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

2. Post Processing Recommender Systems for Diversity

3. Pablo Castells , Neil Hurley , and Saul Vargas . 2022. Novelty and diversity in recommender systems . In Recommender systems handbook . Springer , 603--646. Pablo Castells, Neil Hurley, and Saul Vargas. 2022. Novelty and diversity in recommender systems. In Recommender systems handbook. Springer, 603--646.

4. Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems

5. Beyond accuracy

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