Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28057
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dc.contributor.authorNishioka, Chifumien_UK
dc.contributor.authorGroße-Bölting, Gregoren_UK
dc.contributor.authorScherp, Ansgaren_UK
dc.date.accessioned2018-11-06T14:31:17Z-
dc.date.available2018-11-06T14:31:17Z-
dc.date.issued2015-12-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/28057-
dc.description.abstractWe conduct two experiments to compare different scoring functions for extracted user interests and measure the influence of using older data. We apply our experiments in the domains of computer science and medicine. The first experiment assesses similarity scores between a user's social media profile and a corresponding user's publication profile, in order to evaluate to which extend a user's social media profile reflects his or her professional interests. The second experiment recommends related researchers profiled by their publications based on a user's social media profile. The result revealed that while the functions using spreading activation produce large similarity scores between a user profile and publication profile, the scoring functions with statistical methods (e.g., an extension of BM25 with spreading activation) perform best for recommendation. In terms of the temporal influence, the older data have almost no influence on the performance in the medicine dataset. However, in the computer science dataset, while there is a positive influence in the first experiment, the second experiment demonstrated a negative influence when adding too old data.en_UK
dc.language.isoenen_UK
dc.publisherACMen_UK
dc.relationNishioka C, Große-Bölting G & Scherp A (2015) Influence of time on user profiling and recommending researchers in social media. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW '15), volume 21-22-October-2015. 15th International Conference on Knowledge Technologies and Data-driven Business, Graz, Austria, 21.10.2015-22.10.2015. New York: ACM, p. Article 9. https://doi.org/10.1145/2809563.2809601en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectUser profilingen_UK
dc.subjectsocial mediaen_UK
dc.subjecttemporal analysisen_UK
dc.titleInfluence of time on user profiling and recommending researchers in social mediaen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[Nishioka et al 2015.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1145/2809563.2809601en_UK
dc.citation.jtitleACM International Conference Proceeding Seriesen_UK
dc.citation.volume21-22-October-2015en_UK
dc.citation.spageArticle 9en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailansgar.scherp@stir.ac.uken_UK
dc.citation.btitleProceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW '15)en_UK
dc.citation.conferencedates2015-10-21 - 2015-10-22en_UK
dc.citation.conferencelocationGraz, Austriaen_UK
dc.citation.conferencename15th International Conference on Knowledge Technologies and Data-driven Businessen_UK
dc.citation.isbn9781450337212en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationUniversity of Kielen_UK
dc.contributor.affiliationUniversity of Kielen_UK
dc.contributor.affiliationUniversity of Kielen_UK
dc.identifier.scopusid2-s2.0-84958698792en_UK
dc.identifier.wtid1007264en_UK
dc.contributor.orcid0000-0002-2653-9245en_UK
dc.date.accepted2017-07-24en_UK
dc.date.filedepositdate2018-10-22en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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