Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Toni Gruetze

Ph.D. student at the Infomation Systems Research Group and member of the Knowledge Discovery and Mining Group at Hasso Plattner Institute for Software Systems Engineering

Toni Gruetze

Contact Information

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: E-2-01.1

Phone: +49 331 5509 237

Email: Toni Gruetze

Research Interests

  • Web Mining
  • Distributed Computing
  • Information Retrieval
  • Machine Learning
  • Recommender Systems

Supervisions

  • Master's theses:

    • "Large-Scale Twitter Hashtag Recommendation for Documents" by Gary Yao, 2014
    • "Context-based Tweet Recommendation for News Articles" by Alexander Spivak, 2016

Publications

Topic Shifts in StackOverflow: Ask it like Socrates

Gruetze, Toni and Krestel, Ralf and Naumann, Felix
In Proceedings of the 21st International Conference on Applications of Natual Language to Information Systems (NLDB), volume 9612 pages 213–221, 6 2016 Springer.

DOI: 10.1007/978-3-319-41754-7_18

Abstract:

Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.

BibTeX file

@inproceedings{GruetzeStackOverflow2016,
author = { Gruetze, Toni and Krestel, Ralf and Naumann, Felix },
title = { Topic Shifts in StackOverflow: Ask it like Socrates },
journal = { Lecture Notes in Computer Science },
year = { 2016 },
volume = { 9612 },
pages = { 213--221 },
month = { 6 },
abstract = { Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities. },
publisher = { Springer },
booktitle = { Proceedings of the 21st International Conference on Applications of Natual Language to Information Systems (NLDB) },
priority = { 0 }
}

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last change: Fri, 12 Aug 2016 17:30:26 +0200