  Recommender systems typically suggest to users content similar to what they
consumed in the past. If a user happens to be exposed to strongly polarized
content, she might subsequently receive recommendations which may steer her
towards more and more radicalized content, eventually being trapped in what we
call a "radicalization pathway". In this paper, we study the problem of
mitigating radicalization pathways using a graph-based approach. Specifically,
we model the set of recommendations of a "what-to-watch-next" recommender as a
d-regular directed graph where nodes correspond to content items, links to
recommendations, and paths to possible user sessions. We measure the
"segregation" score of a node representing radicalized content as the expected
length of a random walk from that node to any node representing non-radicalized
content. High segregation scores are associated to larger chances to get users
trapped in radicalization pathways. Hence, we define the problem of reducing
the prevalence of radicalization pathways by selecting a small number of edges
to "rewire", so to minimize the maximum of segregation scores among all
radicalized nodes, while maintaining the relevance of the recommendations. We
prove that the problem of finding the optimal set of recommendations to rewire
is NP-hard and NP-hard to approximate within any factor. Therefore, we turn our
attention to heuristics, and propose an efficient yet effective greedy
algorithm based on the absorbing random walk theory. Our experiments on
real-world datasets in the context of video and news recommendations confirm
the effectiveness of our proposal.
