Efficiency Improvement of Neutrality-Enhanced Recommendation

By Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma. 2013

41 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Jiawei Chen
5. Meng Wang
6. Dietmar Jannach
7. Le Wu
8. Himan Abdollahpouri

Citations in the corpus

• Learning Fair Classifiers (2015, background): “The second strategy consists of modifying existing classifiers to limit discrimination (Calders & Verwer, 2010; Kamiran et al., 2012; Kamishima et al., 2011, 2013; Pedreschi et al., 2009).”
• Prediction with Model-Based Neutrality (2013, unknown): “[13], [14].”
• Multistakeholder recommendation: Survey and research directions (2020, background): “Kamishima et al. (2013) uses mean matching between protected and unprotected groups, but does not control for the difference in variance.”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, methodology): “[108] applied the mean-match regularizer [167] in their information-neutral recommender systems (INRS) to correct popularity bias.”