Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Traub Matthias, Kowald Dominik, Lacic Emanuel, Lex Elisabeth, Schoen Pepjin, Supp Gernot

Smart booking without looking: providing hotel recommendations in the TripRebel portal

Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, i-know 2015, ACM, Graz, Austria, 2015

In this paper, we present a scalable hotel recommender system for TripRebel, a new online booking portal. On the basis of the open-source enterprise search platform Apache Solr, we developed a system architecture with Web-based services to interact with indexed data at large scale as well as to provide hotel recommendations using various state-of-the-art recommender algorithms. We demonstrate the efficiency of our system directly using the live TripRebel portal where, in its current state, hotel alternatives for a given hotel are calculated based on data gathered from the Expedia AffiliateNetwork (EAN).

Lacic Emanuel, Traub Matthias, Kowald Dominik, Lex Elisabeth

ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture

In the Large-Scale Recommender Systems Workshop (LSRS'15) at the 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

In this paper, we present our approach towards an effective scalable recommender framework termed ScaR. Our framework is based on the microservices architecture and exploits search technology to provide real-time recommendations. Since it is our aim to create a system that can be used in a broad range of scenarios, we designed it to be capable of handling various data streams and sources. We show its efficacy and scalability with an initial experiment on how the framework can be used in a large-scale setting.

Lacic Emanuel, Luzhnica Granit, Simon Jörg Peter, Traub Matthias, Lex Elisabeth, Kowald Dominik

Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems

Proceedings of 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

In this paper, we present 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, where methods based on Matrix Factorization would not work. We simulate and evaluate our proposed system using data from the location-based FourSquare system and show that we can provide substantially better recommender accuracy results than a simple MostPopular baseline that is typically used when no interaction data is available.
Kontakt Karriere

Hiermit erkläre ich ausdrücklich meine Einwilligung zum Einsatz und zur Speicherung von Cookies. Weiter Informationen finden sich unter Datenschutzerklärung

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.