I’m taking part in the CrowdRec EU FP7 project together with Gravity R&D, TU Delft, TU Berlin, Moviri, and JCP. With CrowdRec we try to come up with funky new recommendation algorithms and so far we have been doing a pretty good job, see our Session-based recommendations with RNN’s paper.
In 2010 I was awarded a two-year Marie Curie IEF fellowship to undertake research on Context-aware Recommendation at Telefonica Research. The main idea was to leverage context information such as location, temporal data etc. to increase the quality and relevance of the provided recommendations. The EU Commission fund this project really cool and invited me to a workshop on EU projects at the Spanish ministry of Economy.
An innovative context-aware app discovery tool. The application is a proof-of-concept research prototype designed to test and showcase a novel context-aware recommendation algorithm developed together with my colleague Linas Baltrunas from Telefonica Research and collaborator Matthias Böhmer from DFKI. Frappé was recently released on Google Play and we’re currently running a local user study to determine if the app discovery experience we offer is an enriching one. My former colleague Karen Church was also involved in the project helping with the mobile HCI components and user study design / deployment. Any suggestions or comments are welcome. You can contact the team on firstname.lastname@example.org.
CADI was a project of the French Research Agency (ANR). It aimed at exploring the use of Machine Learning methods for Recommender Systems. It involved three academic institutions (LIP6, LIPN, LITIS) and five industry partners (KXEN, Numsight, Mondomix, Spacecode, BAO). The project was coordinated by Françoise Soulié Fogelman.
This was a project of the Austrian Science Fund (FWF) . For this project I conducted a research on Economic Modeling and Machine Learning. It also financed my PhD (thanks FWF!)