It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation

Author:

Moon Jaewan1ORCID,Kim Hye-young1ORCID,Lee Jongwuk1ORCID

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

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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