FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms [article]

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
2022 arXiv   pre-print
Related Item Recommendations (RIRs) are ubiquitous in most online platforms today, including e-commerce and content streaming sites. These recommendations not only help users compare items related to a given item, but also play a major role in bringing traffic to individual items, thus deciding the exposure that different items receive. With a growing number of people depending on such platforms to earn their livelihood, it is important to understand whether different items are receiving their
more » ... esired exposure. To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure. To mitigate this exposure bias, we introduce multiple flexible interventions (FaiRIR) in the RIR pipeline. We instantiate these mechanisms with two well-known algorithms for constructing related item recommendations -- rating-SVD and item2vec -- and show on real-world data that our mechanisms allow for a fine-grained control on the exposure distribution, often at a small or no cost in terms of recommendation quality, measured in terms of relatedness and user satisfaction.
arXiv:2204.00241v1 fatcat:ncnqwhesdreizicsp7pvwyaafa