It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation
Author:
Affiliation:
1. Sungkyunkwan University, Suwon, Republic of Korea
Funder
Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3539618.3591704
Reference45 articles.
1. Lei Chen Le Wu Richang Hong Kun Zhang and Meng Wang. 2020. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In AAAI. 27--34. Lei Chen Le Wu Richang Hong Kun Zhang and Meng Wang. 2020. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In AAAI. 27--34.
2. Jin Yao Chin Yile Chen and Gao Cong. 2022. The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?. In WSDM K. Selcuk Candan Huan Liu Leman Akoglu Xin Luna Dong and Jiliang Tang (Eds.). 141--149. Jin Yao Chin Yile Chen and Gao Cong. 2022. The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?. In WSDM K. Selcuk Candan Huan Liu Leman Akoglu Xin Luna Dong and Jiliang Tang (Eds.). 141--149.
3. Jeongwhan Choi Jinsung Jeon and Noseong Park. 2021. LT-OCF: Learnable-Time ODE-based Collaborative Filtering. In CIKM. 251--260. Jeongwhan Choi Jinsung Jeon and Noseong Park. 2021. LT-OCF: Learnable-Time ODE-based Collaborative Filtering. In CIKM. 251--260.
4. Colin Cooper Sang-Hyuk Lee Tomasz Radzik and Yiannis Siantos. 2014. Random walks in recommender systems: exact computation and simulations. In WWW. 811--816. Colin Cooper Sang-Hyuk Lee Tomasz Radzik and Yiannis Siantos. 2014. Random walks in recommender systems: exact computation and simulations. In WWW. 811--816.
5. A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research;Dacrema Maurizio Ferrari;ACM Trans. Inf. Syst.,2021
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