Learning Explicit User Interest Boundary for Recommendation-Reference-Cited by-同舟云学术

Learning Explicit User Interest Boundary for Recommendation

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
language:
Short-container-title:

Author:

Zhuo Jianhuan¹,Zhu Qiannan²,Yue Yinliang¹,Zhao Yuhong³

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China

2. Gaoling School of Artificial Intelligence, Renmin University of China, China and Beijing Key Laboratory of Big Data Management and Analysis Methods, China

3. Institute of Information Engineering, Chinese Academy of Sciences, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3511971

Reference35 articles.

1. Probabilistic collaborative filtering with negative cross entropy

2. Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)

3. Efficient Neural Matrix Factorization without Sampling for Recommendation

4. Deep Neural Networks for YouTube Recommendations

5. Jingtao Ding Yuhan Quan Xiangnan He Yong Li and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data. (2019) 2230–2236. Jingtao Ding Yuhan Quan Xiangnan He Yong Li and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data. (2019) 2230–2236.

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Denoising and Augmented Negative Sampling for Collaborative Filtering;ACM Transactions on Recommender Systems;2024-08-28

2. Toward a Better Understanding of Loss Functions for Collaborative Filtering;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

3. Augmented Negative Sampling for Collaborative Filtering;Proceedings of the 17th ACM Conference on Recommender Systems;2023-09-14

4. DAS-GNN: Denoising autoencoder integrated with self-supervised learning in graph neural network-based recommendations;Applied Intelligence;2022-12-28

5. Unifying multi-associations through hypergraph for bundle recommendation;Knowledge-Based Systems;2022-11