Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method-Reference-Cited by-同舟云学术

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Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method

Published:2024-08-24 Issue: Volume:41 Page:3714-3723
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Yang Chen¹^ORCID,Dai Sunhao²^ORCID,Hou Yupeng³^ORCID,Zhao Wayne Xin²^ORCID,Xu Jun²^ORCID,Song Yang⁴^ORCID,Zhu Hengshu⁵^ORCID

Affiliation:

1. Nanbeige Lab, BOSS Zhipin & Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China

2. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China

3. University of California, San Diego, La Jolla, USA

4. Nanbeige Lab, BOSS Zhipin, Beijing, China

5. Career Science Lab, BOSS Zhipin, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671734

Reference53 articles.

1. Improving Matching Process in Social Network Using Implicit and Explicit User Information

2. Causal embeddings for recommendation

3. Bias and debias in recommender system: A survey and future directions;Chen Jiawei;ACM Transactions on Information Systems,2023

4. Jiaxing Chen, Hongzhi Liu, Hongrui Guo, Yingpeng Du, Zekai Wang, Yang Song, and Zhonghai Wu. 2023. Bilateral Sequential Hypergraph Convolution Network for Reciprocal Recommendation. In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 974--979.

5. Who Marries Whom and Why

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