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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Matakos, Antonis
dc.contributor.author Tu, Sijing
dc.contributor.author Gionis, Aristides
dc.date.accessioned 2020-11-30T08:13:03Z
dc.date.available 2020-11-30T08:13:03Z
dc.date.issued 2020-09-01
dc.identifier.citation Matakos , A , Tu , S & Gionis , A 2020 , ' Tell me something my friends do not know : diversity maximization in social networks ' , Knowledge and Information Systems , vol. 62 , no. 9 , pp. 3697-3726 . https://doi.org/10.1007/s10115-020-01456-1 en
dc.identifier.issn 0219-1377
dc.identifier.issn 0219-3116
dc.identifier.other PURE UUID: 381d4f2b-37be-479b-abea-c4da3581c6d4
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/381d4f2b-37be-479b-abea-c4da3581c6d4
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85084140645&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/53400673/Matakos2020_Article_TellMeSomethingMyFriendsDoNotK.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/61679
dc.description | openaire: EC/H2020/654024/EU//SoBigData
dc.description.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 nonapproximable 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. en
dc.format.extent 30
dc.format.extent 3697-3726
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Springer London
dc.relation info:eu-repo/grantAgreement/EC/H2020/654024/EU//SoBigData
dc.relation.ispartofseries Knowledge and Information Systems en
dc.relation.ispartofseries Volume 62, issue 9 en
dc.rights openAccess en
dc.title Tell me something my friends do not know en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Helsinki Institute for Information Technology (HIIT)
dc.subject.keyword Combinatorial optimization
dc.subject.keyword Diversity maximization
dc.subject.keyword Filter bubble
dc.subject.keyword Greedy algorithms
dc.subject.keyword Quadratic knapsack
dc.identifier.urn URN:NBN:fi:aalto-2020113020524
dc.identifier.doi 10.1007/s10115-020-01456-1
dc.type.version publishedVersion

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