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

Learning Temporal Tagging Behaviour

Toni Gruetze, Gary Yao, Ralf Krestel
In Proceedings of the 24th International Conference on World Wide Web Companion (WWW), pages 1333–1338, 5 2015 ACM.

DOI: 10.1145/2740908.2741701

Abstract:

Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags.

BibTeX file

@inproceedings{Gruetze2015TempWeb,
author = { Toni Gruetze, Gary Yao, Ralf Krestel },
title = { Learning Temporal Tagging Behaviour },
year = { 2015 },
pages = { 1333--1338 },
month = { 5 },
abstract = { Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags. },
publisher = { ACM },
booktitle = { Proceedings of the 24th International Conference on World Wide Web Companion (WWW) },
priority = { 0 }
}

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last change: Fri, 12 Aug 2016 17:29:48 +0200