Strong Know-Center presence at ACM RecSys 2015

The 9th ACM Conference on Recommender Systems is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. With three accepted publications, the Know-Center shows a strong presence at one of the most important conferences regarding recommender systems research.

Taking place in Vienna, Austria from September 16 – 20, 2015, the 9th ACM Conference on Recommender Systems (RecSys 2015) conference brings together the main international research groups working on recommender systems, along with many of the world’s leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender systems research.

Know-Center’s Social Computing Area, represented by Emanuel Lacić, Dominik Kowald and Matthias Traub will present three publications at RecSys 2015:

  •  “Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems
    This publication presents work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for “extreme” cold-start users with absolutely no item interaction data available. The presented work was funded within the Learning Layers EU research project.
    The paper is available for download here.
  •  Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study”
    The goal of this study is to provide researchers and developers of tag-based recommender systems with an extensive, transparent and reproducible evaluation of state-of-the-art tag recommender algorithms in real-world folksonomies. The presented work was funded within the Learning Layers EU research project.
    The paper is available for download here.
  • ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture
    This publication presents an effective scalable recommender framework termed ScaR. The framework is based on the microservices architecture and exploits search technology to provide real-time recommendations. The presented work was funded within the Learning Layers EU research project.

 

Complete bibliographies:

  • Emanuel Lacic, Dominik Kowald, Matthias Traub, Granit Luzhnica, Joerg Simon and Elisabeth Lex. “Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems”, in the Poster Proceedings of the 9th ACM Conference on Recommender Systems (RecSys 2015)
  • Dominik Kowald and Elisabeth Lex. “Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study”, in the Proceedings of the 9th ACM Conference on Recommender Systems (RecSys 2015) ). ACM, New York, NY, USA, 265-268.
  • Emanuel Lacic, Matthias Traub, Dominik Kowald and Elisabeth Lex. “ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture”, in the Large-Scale Recommender Systems Workshop (LSRS) held in conjunction with the RecSys 2015.

 

Back
Contact

Wenn Sie diese Seite nutzen stimmen Sie der Verwendung von Cookies zu mehr Information

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.

Close