Transfer Learning for Human & AI

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making . Just like users have personalized preferences on items, users’ demands for fairness are also personalized in many scenarios . To this end, weintroduce a framework for achieving counterfactually fair recommendationsthrough adversary learning by generating feature-independent user embeddings for recommendation . The framework allows recommender systems to achieve personalized fairness for users while also covering non-personalizedsituations. Experiments on two real-world datasets with shallow and deeprecommendation algorithms show that our method can generate fairerrecommendations for users with a desirable recommendation performance. The method can be used in real-time data with a desired recommendation performance, according to the authors . The method is published in The New Scientist

Author(s) : Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang

Links : PDF - Abstract

Code :


Keywords : users - personalized - fairness - method - recommendation -

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