Recommendation Independence. Kamishima, T.; Akaho, S.; Asoh, H.; and Sakuma, J. 81:187–201.
Recommendation Independence [link]Paper  abstract   bibtex   
This paper studies a recommendation algorithm whose outcomes are not influenced by specified information. It is useful in contexts potentially unfair decision should be avoided, such as job-applicant recommendations that are not influenced by socially sensitive information. An algorithm that could exclude the influence of sensitive information would thus be useful for job-matching with fairness. We call the condition between a recommendation outcome and a sensitive feature Recommendation Independence, which is formally defined as statistical independence between the outcome and the feature. Our previous independence-enhanced algorithms simply matched the means of predictions between sub-datasets consisting of the same sensitive value. However, this approach could not remove the sensitive information represented by the second or higher moments of distributions. In this paper, we develop new methods that can deal with the second moment, i.e., variance, of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that our new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, we explore potential applications for independence-enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity.
@article{kamishima_recommendation_2018,
	title = {Recommendation Independence},
	volume = {81},
	url = {http://proceedings.mlr.press/v81/kamishima18a.html},
	abstract = {This paper studies a recommendation algorithm whose outcomes are not
influenced by specified information. It is useful in contexts potentially
unfair decision should be avoided, such as job-applicant recommendations
that are not influenced by socially sensitive information. An algorithm
that could exclude the influence of sensitive information would thus be
useful for job-matching with fairness. We call the condition between a
recommendation outcome and a sensitive feature Recommendation
Independence, which is formally defined as statistical independence
between the outcome and the feature. Our previous independence-enhanced
algorithms simply matched the means of predictions between sub-datasets
consisting of the same sensitive value. However, this approach could not
remove the sensitive information represented by the second or higher
moments of distributions. In this paper, we develop new methods that can
deal with the second moment, i.e., variance, of recommendation outcomes
without increasing the computational complexity. These methods can more
strictly remove the sensitive information, and experimental results
demonstrate that our new algorithms can more effectively eliminate the
factors that undermine fairness. Additionally, we explore potential
applications for independence-enhanced recommendation, and discuss its
relation to other concepts, such as recommendation diversity.},
	pages = {187--201},
	author = {Kamishima, Toshihiro and Akaho, Shotaro and Asoh, Hideki and Sakuma, Jun},
	date = {2018}
}
Downloads: 0