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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 contributionGraph-Based and Personalized Content Recommendations for Software Developers
Original languageTurkish
Title of host publication2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728172064
DOIs
Publication statusPublished - 5 Oct 2020
Event28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey
Duration: 5 Oct 20207 Oct 2020

Publication series

Name2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

Conference

Conference28th Signal Processing and Communications Applications Conference, SIU 2020
Country/TerritoryTurkey
CityGaziantep
Period5/10/207/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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