Helsinki Algorithms Seminar: Cigdem Aslay "Maximizing the diversity of exposure in a social network"
Maximizing the diversity of exposure in a social network
Postdoctoral Researcher, Aalto University
In this talk I will present our recent result that provides a novel approach to contribute towards bursting filter bubbles. We formulate the problem as a task of recommending news articles to selected users with the aim to maximize the overall diversity of information exposure in a social network. We consider a realistic setting where we take into account the political leanings of users and articles, and the probability of users to further share articles. We show that this problem is a challenging generalization of the influence maximization problem, which is NP-hard, and it corresponds to the problem of maximizing a monotone submodular function subject to a matroid constraint on the allocation of articles to users. We introduce the notion of random reverse co-exposure sets and a set of estimation techniques based on martingales for efficiently estimating expected diversity of exposure. Accordingly, we devise a scalable instantiation of the greedy algorithm that provides (1/2-epsilon)-approximation to the optimum with high probability.
Helsinki Algorithms Seminar is a weekly meeting of researchers in the Helsinki area interested in the art of algorithms and algorithm design, broadly interpreted to cover both theoretical ideas and algorithm engineering on concrete computing platforms. In most cases we have a presentation prepared for each meeting to communicate an idea, a recent result, work-in-progress, or demo, but this should not be at the expense of discussion and simply having fun with algorithms.
Our affiliations are with Aalto University and the University of Helsinki, and accordingly our activities alternate between the Otaniemi Campus of Aalto University and the Kumpula Campus of University of Helsinki, catalyzed by the Helsinki Institute for Information Technology HIIT, under the Algorithmic Data Analysis (ADA) programme.