ADVERTISING RECOMMENDATION SYSTEM BASED ON DYNAMIC DATA ANALYSIS ON TURKISH SPEAKING TWITTER USERS.
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| Title: | ADVERTISING RECOMMENDATION SYSTEM BASED ON DYNAMIC DATA ANALYSIS ON TURKISH SPEAKING TWITTER USERS. |
|---|---|
| Alternate Title: | Preporučeni sustav oglašavanja zasnovan na dinamičkoj analizi podataka turskih korisnika Twittera. |
| Authors: | Sevli, Onur1 onursevli@mehmetakif.edu.tr, Küçüksille, Ecir Uğur2 ecirkucuksille@sdu.edu.tr |
| Source: | Technical Gazette / Tehnički Vjesnik. Mar/Apr2017, Vol. 24 Issue 2, p571-578. 8p. |
| Subjects: | Twitter (Web resource), Feedback control systems, Social networks, Real-time control, Data analysis |
| Abstract (English): | Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user's profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback. [ABSTRACT FROM AUTHOR] |
| Abstract (Croatian): | Online okruženja, a posebno društvene mreže postala su snažna alternative objavljivanju oglasa. Za učinkovito oglašavanje važno je da se sadržaj poistovjećuje s očekivanjima ciljane publike. Uzimajući u obzir da se očekivanja mogu s vremenom promijeniti, potrebno je u realnom vremenu i dinamički prepoznati orijentaciju korisnika. U ovom su se radu u realnom vremenu analizirale poruke turskih korisnika Twittera i identificirala njihova trenutna očekivanja. U tu je svrhu dizajnirana web usluga koja analizira profil korisnika i daje oglase koji najbolje odgovaraju očekivanjima. Za filtriranje odgovarajućeg sadržaja oglašavanja korištena je metoda nazvana heuristička metoda odstranjivanja suvišnog (Heuristic Pruning Method - HPM). Razvijeni sustav je testiran na grupi volontera, aktivnih korisnika Twittera, a učinkovitost sustava se pokazala dobivenom povratnom informacijom-feedbackom. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 122569031 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ADVERTISING RECOMMENDATION SYSTEM BASED ON DYNAMIC DATA ANALYSIS ON TURKISH SPEAKING TWITTER USERS. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Preporučeni sustav oglašavanja zasnovan na dinamičkoj analizi podataka turskih korisnika Twittera. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sevli%2C+Onur%22">Sevli, Onur</searchLink><relatesTo>1</relatesTo><i> onursevli@mehmetakif.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Küçüksille%2C+Ecir+Uğur%22">Küçüksille, Ecir Uğur</searchLink><relatesTo>2</relatesTo><i> ecirkucuksille@sdu.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. Mar/Apr2017, Vol. 24 Issue 2, p571-578. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Twitter+%28Web+resource%29%22">Twitter (Web resource)</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+control%22">Real-time control</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user's profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Croatian) Group: Ab Data: Online okruženja, a posebno društvene mreže postala su snažna alternative objavljivanju oglasa. Za učinkovito oglašavanje važno je da se sadržaj poistovjećuje s očekivanjima ciljane publike. Uzimajući u obzir da se očekivanja mogu s vremenom promijeniti, potrebno je u realnom vremenu i dinamički prepoznati orijentaciju korisnika. U ovom su se radu u realnom vremenu analizirale poruke turskih korisnika Twittera i identificirala njihova trenutna očekivanja. U tu je svrhu dizajnirana web usluga koja analizira profil korisnika i daje oglase koji najbolje odgovaraju očekivanjima. Za filtriranje odgovarajućeg sadržaja oglašavanja korištena je metoda nazvana heuristička metoda odstranjivanja suvišnog (Heuristic Pruning Method - HPM). Razvijeni sustav je testiran na grupi volontera, aktivnih korisnika Twittera, a učinkovitost sustava se pokazala dobivenom povratnom informacijom-feedbackom. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20151020205558 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 571 Subjects: – SubjectFull: Twitter (Web resource) Type: general – SubjectFull: Feedback control systems Type: general – SubjectFull: Social networks Type: general – SubjectFull: Real-time control Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: ADVERTISING RECOMMENDATION SYSTEM BASED ON DYNAMIC DATA ANALYSIS ON TURKISH SPEAKING TWITTER USERS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sevli, Onur – PersonEntity: Name: NameFull: Küçüksille, Ecir Uğur IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar/Apr2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 24 – Type: issue Value: 2 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
| ResultId | 1 |
