Fairness among New Items in Cold Start Recommender Systems-Reference-Cited by-同舟云学术

Fairness among New Items in Cold Start Recommender Systems

Published:2021-07-11 Issue: Volume: Page:
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Container-title:Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Zhu Ziwei¹,Kim Jingu²,Nguyen Trung²,Fenton Aish²,Caverlee James¹

Affiliation:

1. Texas A&M University, College Station, TX, USA

2. Netflix, Los Gatos, CA, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3404835.3462948

Reference43 articles.

1. Mart'in Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard etal 2016. Tensorflow: a system for large-scale machine learning. In OSDI. Mart'in Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard et al. 2016. Tensorflow: a system for large-scale machine learning. In OSDI.

2. Controlling Popularity Bias in Learning-to-Rank Recommendation

3. RecSys Challenge 2017

4. Cold-Start Item and User Recommendation with Decoupled Completion and Transduction

5. Fairness in Recommendation Ranking through Pairwise Comparisons

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