Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering-Reference-Cited by-同舟云学术

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

Published:2022-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Zhao Minghao¹,Wu Le²,Liang Yile¹,Chen Lei²,Zhang Jian³,Deng Qilin¹,Wang Kai¹,Shen Xudong¹,Lv Tangjie¹,Wu Runze¹

Affiliation:

1. Fuxi AI Lab, NetEase Games, Hangzhou, China

2. Hefei University of Technology, Hefei, China

3. Zhejiang University of Technology, Hangzhou, China

Funder

National Key R&D Program of China

the Young Elite Scientists Sponsorship Program by CAST and ISZS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477495.3532005

Reference51 articles.

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2. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In FLAIRS. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In FLAIRS.

3. Himan Abdollahpouri , Masoud Mansoury , Robin Burke , and Bamshad Mobasher . 2019 . The Unfairness of Popularity Bias in Recommendation. In RecSys Workshop. Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The Unfairness of Popularity Bias in Recommendation. In RecSys Workshop.

4. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

5. Rianne van den Berg , Thomas N Kipf , and Max Welling . 2017. Graph convolutional matrix completion. KDD ( 2017 ). Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. KDD (2017).

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