  Deep learning-based recommender systems (DRSs) are increasingly and widely
deployed in the industry, which brings significant convenience to people's
daily life in different ways. However, recommender systems are also shown to
suffer from multiple issues,e.g., the echo chamber and the Matthew effect, of
which the notation of "fairness" plays a core role.While many fairness
notations and corresponding fairness testing approaches have been developed for
traditional deep classification models, they are essentially hardly applicable
to DRSs. One major difficulty is that there still lacks a systematic
understanding and mapping between the existing fairness notations and the
diverse testing requirements for deep recommender systems, not to mention
further testing or debugging activities. To address the gap, we propose
FairRec, a unified framework that supports fairness testing of DRSs from
multiple customized perspectives, e.g., model utility, item diversity, item
popularity, etc. We also propose a novel, efficient search-based testing
approach to tackle the new challenge, i.e., double-ended discrete particle
swarm optimization (DPSO) algorithm, to effectively search for hidden fairness
issues in the form of certain disadvantaged groups from a vast number of
candidate groups. Given the testing report, by adopting a simple re-ranking
mitigation strategy on these identified disadvantaged groups, we show that the
fairness of DRSs can be significantly improved. We conducted extensive
experiments on multiple industry-level DRSs adopted by leading companies. The
results confirm that FairRec is effective and efficient in identifying the
deeply hidden fairness issues, e.g., achieving 95% testing accuracy with half
to 1/8 time.
