DC FieldValueLanguage
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorTsapatsoulis, Nicolas-
dc.date.accessioned2024-10-29T10:54:08Z-
dc.date.available2024-10-29T10:54:08Z-
dc.date.issued2017-05-04-
dc.identifierscopus-85020189500-
dc.identifier.isbn978-1-5090-5880-8-
dc.identifier.other85020189500-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2848-
dc.description.abstractThis paper proposes an innovative relevance feedback algorithm for wall-content selection in social media. The procedure results in an iterative loop, which recursively updates a weighted distance. The distance is then used for finding multimedia items that are relevant to a user's preferences. To do so, the activity log of the user under investigation is considered and his/her attention at previous intervals is analyzed. Another novel point of the proposed approach is the incorporation of friends' attention into the relevance feedback scheme. In particular, interactions among users and posted multimedia items are considered as an explicit crowdsourcing activity. By this way some multimedia items receive more attention, while some others receive less or no attention. By analyzing these social interactions, a social computing framework is formed, which affects the evolution of the content selection process. Overall, the iterative relevance feedback algorithm takes into consideration visual features, activity log and social attention, in order to select the wall information of each social media user. Experimental results and comparisons on real data, exhibit the advantages of the proposed scheme and future directions are also discussed.en_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the 2016 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical, and Social Computing and IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016en_US
dc.subjectActivity logen_US
dc.subjectCrowdsourcingen_US
dc.subjectMultimedia itemen_US
dc.subjectRelevance Feedback (RF)en_US
dc.subjectSocial computingen_US
dc.subjectSocial mediaen_US
dc.titleWall-content selection in social media: a revelance feedback scheme based on explicit crowdsourcingen_US
dc.typeConference Paperen_US
dc.relation.conference2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 15-18 December 2016, Chengdu, Chinaen_US
dc.identifier.doi10.1109/iThings-GreenCom-CPSCom-SmartData.2016.122en_US
dc.identifier.scopus2-s2.0-85020189500-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage534en_US
dc.identifier.epage539en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusnot verifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
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