  Rankings, especially those in search and recommendation systems, often
determine how people access information and how information is exposed to
people. Therefore, how to balance the relevance and fairness of information
exposure is considered as one of the key problems for modern IR systems. As
conventional ranking frameworks that myopically sorts documents with their
relevance will inevitably introduce unfair result exposure, recent studies on
ranking fairness mostly focus on dynamic ranking paradigms where result
rankings can be adapted in real-time to support fairness in groups (i.e.,
races, genders, etc.). Existing studies on fairness in dynamic learning to
rank, however, often achieve the overall fairness of document exposure in
ranked lists by significantly sacrificing the performance of result relevance
and fairness on the top results. To address this problem, we propose a fair and
unbiased ranking method named Maximal Marginal Fairness (MMF). The algorithm
integrates unbiased estimators for both relevance and merit-based fairness
while providing an explicit controller that balances the selection of documents
to maximize the marginal relevance and fairness in top-k results. Theoretical
and empirical analysis shows that, with small compromises on long list
fairness, our method achieves superior efficiency and effectiveness comparing
to the state-of-the-art algorithms in both relevance and fairness for top-k
rankings.
