Preference eigensystems for fair ranking

The problem of fair decision had been studied within information systems for a long time. In the event of Information Retrieval and Recommendation, the need of accessing information by means of rankings complicates the search for fairness because the exposure of an item depends on the user’s attention and on the rank of the item. Starting from the observation that a ranking is the result of a series of preferences based on a variety of criteria, this paper describes Preference Fair Ranking (PFR). In a nutshell, matrices are defined containing the preferences between items according to different criteria and the eigenvectors of a linear combination of the matrices that establish the best ranking are found. The paper also reports on the experiments carried out by using two large public test collections for fair information retrieval, i.e. 2021 and 2022 TREC Fair tracks and by comparing the proposed method with two well-known fair ranking algorithms. The results show that the proposed method significantly outperform the other two methods. In particular, PFR showed to be superior in fairness to the baseline methods in terms of the TREC Fair track measures. Besides retrieval effectiveness and fairness, PFR is computationally efficient and helps the flexible design of multiple criteria ranking. • A matrix encodes item preferences according to one criterion. • A linear combination of matrices models multiple preference criteria. • The principal eigenvector provides the unique effective fair ranking. • The experiments confirm effectiveness and efficiency.