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

Translated title of the contribution: Graph-Based and Personalized Content Recommendations for Software Developers

Elif Ak, Ayse Tosun

Department of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Translated title of the contribution	Graph-Based and Personalized Content Recommendations for Software Developers
Original language	Turkish
Title of host publication	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728172064
DOIs	https://doi.org/10.1109/SIU49456.2020.9302381
Publication status	Published - 5 Oct 2020
Event	28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Duration: 5 Oct 2020 → 7 Oct 2020

Publication series

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

Conference

Conference	28th Signal Processing and Communications Applications Conference, SIU 2020
Country/Territory	Turkey
City	Gaziantep
Period	5/10/20 → 7/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Access to Document

10.1109/SIU49456.2020.9302381

Cite this

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 Article 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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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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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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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

Abstract

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