Tell me something my friends do not know

No Thumbnail Available
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal

Other link related to publication
Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
327-336
Series
2018 IEEE International Conference on Data Mining (ICDM), IEEE International Conference on Data Mining (ICDM)
Abstract
Social media have a great potential to improve information dissemination in our society, yet, they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper we propose a novel approach to address the problem of breaking filter bubbles in social media. We do so by aiming to maximize the diversity of the information exposed to connected social-media users. We formulate the problem of maximizing the diversity of exposure as a quadratic-knapsack problem. We show that the proposed diversity-maximization problem is inapproximable, and thus, we resort to polynomial non-approximable algorithms, inspired by solutions developed for the quadratic knapsack problem, as well as scalable greedy heuristics. We complement our algorithms with instance-specific upper bounds, which are used to provide empirical approximation guarantees for the given problem instances. Our experimental evaluation shows that a proposed greedy algorithm followed by randomized local search is the algorithm of choice given its quality-vs.-efficiency trade-off.
Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
Diversity maximization, Filter bubble, Quadratic knapsack
Other note
Citation
Matakos , A & Gionis , A 2018 , Tell me something my friends do not know : Diversity maximization in social networks . in 2018 IEEE International Conference on Data Mining, ICDM 2018 . , 8594857 , IEEE International Conference on Data Mining (ICDM) , IEEE , pp. 327-336 , IEEE International Conference on Data Mining , Singapore , Singapore , 17/11/2018 . https://doi.org/10.1109/ICDM.2018.00048