1 research outputs found
Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has
been the subject of extensive recent research. It is especially important in
multi-sided recommendation platforms where it may be crucial to optimize
utilities not just for the end user, but also for other actors such as item
sellers or producers who desire a fair representation of their items. Existing
solutions do not properly address various aspects of multi-sided fairness in
recommendations as they may either solely have one-sided view (i.e. improving
the fairness only for one side), or do not appropriately measure the fairness
for each actor involved in the system. In this thesis, I aim at first
investigating the impact of unfair recommendations on the system and how these
unfair recommendations can negatively affect major actors in the system. Then,
I seek to propose solutions to tackle the unfairness of recommendations. I
propose a rating transformation technique that works as a pre-processing step
before building the recommendation model to alleviate the inherent popularity
bias in the input data and consequently to mitigate the exposure unfairness for
items and suppliers in the recommendation lists. Also, as another solution, I
propose a general graph-based solution that works as a post-processing approach
after recommendation generation for mitigating the multi-sided exposure bias in
the recommendation results. For evaluation, I introduce several metrics for
measuring the exposure fairness for items and suppliers, and show that these
metrics better capture the fairness properties in the recommendation results. I
perform extensive experiments to evaluate the effectiveness of the proposed
solutions. The experiments on different publicly-available datasets and
comparison with various baselines confirm the superiority of the proposed
solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi