Lacic Emanuel, Luzhnica Granit, Simon Jörg Peter, Traub Matthias, Lex Elisabeth, Kowald Dominik
2015
Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems
Proceedings of 9th International Conference on Recommender Systems RecSys'2015 ACM Vienna, Austria
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.
Simon Jörg Peter, Pammer-Schindler Viktoria, Schmidt Peter
2015
An Energy Efficient Implementation of Differential Synchronization on Mobile Devices
Proceedings of 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) London
Synchronisation algorithms are central components of collab- orative editing software. The energy efficiency for such algo- rithms becomes of interest to a wide community of mobile application developers. In this paper we explore the differen- tial synchronisation (diffsync) algorithm with respect to en- ergy consumption on mobile devices.We identify three areas for optimisation: a.) Empty cycles where diffsync is executed although no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. We propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app.