Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi

Elif Ak; Ayse Tosun

doi:10.1109/SIU49456.2020.9302381

Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi

Elif Ak, Ayse Tosun

Bilgisayar Mühendisliği Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

Informal learning through online resources like Stack Exchange is quite popular among the software developers to ask or seek a software-related content. Personalized content recommendation for software developers is still under-explored. With years of accumulation, Stack Exchange has collected thousand of questions and answers in many fields, particularly in software engineering. In this paper, we propose a recommendation system that extracts topics from Stack Exchange and considers the developer's topical navigation from browser search logs to recommend personalized content (i.e. Stack Exchange posts) to the developer. The results shows that our trained model predicts the topics with 92% accuracy, and it is found highly reliable and transparent in terms of recommendation.

Tercüme edilen katkı başlığı	Graph-Based and Personalized Content Recommendations for Software Developers
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728172064
DOI'lar	https://doi.org/10.1109/SIU49456.2020.9302381
Yayın durumu	Yayınlandı - 5 Eki 2020
Etkinlik	28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Süre: 5 Eki 2020 → 7 Eki 2020

Yayın serisi

Adı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	28th Signal Processing and Communications Applications Conference, SIU 2020
Ülke/Bölge	Turkey
Şehir	Gaziantep
Periyot	5/10/20 → 7/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Keywords

Stack Exchange
Word2Vec.
graph-based approach
personalized content recommendation

Belgeye Erişim

10.1109/SIU49456.2020.9302381

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Ak, E., & Tosun, A. (2020). Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings Makale 9302381 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU49456.2020.9302381

Ak, Elif ; Tosun, Ayse. / Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi. 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

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title = "Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi",

abstract = "Informal learning through online resources like Stack Exchange is quite popular among the software developers to ask or seek a software-related content. Personalized content recommendation for software developers is still under-explored. With years of accumulation, Stack Exchange has collected thousand of questions and answers in many fields, particularly in software engineering. In this paper, we propose a recommendation system that extracts topics from Stack Exchange and considers the developer's topical navigation from browser search logs to recommend personalized content (i.e. Stack Exchange posts) to the developer. The results shows that our trained model predicts the topics with 92% accuracy, and it is found highly reliable and transparent in terms of recommendation.",

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author = "Elif Ak and Ayse Tosun",

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Ak, E & Tosun, A 2020, Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi. in 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings., 9302381, 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 28th Signal Processing and Communications Applications Conference, SIU 2020, Gaziantep, Turkey, 5/10/20. https://doi.org/10.1109/SIU49456.2020.9302381

Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi. / Ak, Elif ; Tosun, Ayse.
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9302381 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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T1 - Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi

AU - Ak, Elif

AU - Tosun, Ayse

PY - 2020/10/5

Y1 - 2020/10/5

N2 - Informal learning through online resources like Stack Exchange is quite popular among the software developers to ask or seek a software-related content. Personalized content recommendation for software developers is still under-explored. With years of accumulation, Stack Exchange has collected thousand of questions and answers in many fields, particularly in software engineering. In this paper, we propose a recommendation system that extracts topics from Stack Exchange and considers the developer's topical navigation from browser search logs to recommend personalized content (i.e. Stack Exchange posts) to the developer. The results shows that our trained model predicts the topics with 92% accuracy, and it is found highly reliable and transparent in terms of recommendation.

AB - Informal learning through online resources like Stack Exchange is quite popular among the software developers to ask or seek a software-related content. Personalized content recommendation for software developers is still under-explored. With years of accumulation, Stack Exchange has collected thousand of questions and answers in many fields, particularly in software engineering. In this paper, we propose a recommendation system that extracts topics from Stack Exchange and considers the developer's topical navigation from browser search logs to recommend personalized content (i.e. Stack Exchange posts) to the developer. The results shows that our trained model predicts the topics with 92% accuracy, and it is found highly reliable and transparent in terms of recommendation.

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KW - Word2Vec.

KW - graph-based approach

KW - personalized content recommendation

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Ak E , Tosun A. Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9302381. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). doi: 10.1109/SIU49456.2020.9302381

Yazilim Gelistiricilere Ozel Graf Tabanli ve Kisisellestirilmis Icerik Tavsiye Sistemi

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap