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See detailApproaches for collaborative filtering in distributed environments
Gratz, Patrick UL

Doctoral thesis (2010)

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See detailCollaborative Filtering via Epidemic Aggregation in Distributed Virtual Environments
Gratz, Patrick UL; Botev, Jean UL

in Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom) (2009)

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See detailSelf-Organizing Collaborative Filtering in Global-Scale Massive Multi-User Virtual Environments
Höhfeld, Alexander; Gratz, Patrick UL; Beck, Angelo et al

in Proceedings of the 24th Annual ACM Symposium on Applied Computing (SAC) (2009)

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See detailDelay-Tolerant Collaborative Filtering
Gratz, Patrick UL; Leclerc, Tom

in Proceedings of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (2009)

Recommender systems using collaborative filtering are a well-established technique to overcome information overload in today's digital society. Currently, predominant collaborative filtering systems ... [more ▼]

Recommender systems using collaborative filtering are a well-established technique to overcome information overload in today's digital society. Currently, predominant collaborative filtering systems mostly depend on huge centralized databases to store user preferences and furthermore are only available when connected to Internet. In this paper, we consider an incremental recommender system for highly dynamic mobile environments where no central global knowledge is available and communication links are rather unreliable in comparison to static networks. We present an algorithm that aims to reach a reasonable prediction coverage and accuracy while keeping the amount of additional network overhead as small as possible, maximizing the performance of our system. For this purpose, the presented algorithm is based on a delay-tolerant broadcasting mechanism on top of a weighted cluster topology. Evaluation results show that in terms of accuracy and coverage the results of the presented algorithm converge on those obtained from a global knowledge scenario, even in the case of message loss. [less ▲]

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See detailAd Hoc Collaborative Filtering for Mobile Networks
Gratz, Patrick UL; Andronache, Adrian UL; Rothkugel, Steffen UL

in Sensor Networks, Ubiquitous, and Trustworthy Computing, International Conference on (2008)

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See detailOptimizing Distributed Collaborative Filtering in Mobile Networks
Gratz, Patrick UL; Andronache, Adrian UL

in Proceedings of the 2008 High Performance Computing & Simulation Conference (2008)

Collaborative filtering mechanisms filter information by using collaboration among multiple data sources, typically involving large data sets. In this work we optimize the communication and computational ... [more ▼]

Collaborative filtering mechanisms filter information by using collaboration among multiple data sources, typically involving large data sets. In this work we optimize the communication and computational load of a distributed collaborative filtering protocol designed to augment the information exchange in mobile networks. [less ▲]

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See detailWarum und wie Schüler ab 8 Jahren Programmieren lernen sollten
Esch, Markus UL; Gratz, Patrick UL; Jakoby, Jörg et al

in Lecture Notes in Informatics (LNI), DeLFI 2008: Die 6. E-Learning Fachtagung Informatik (2008)

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See detailCollaborative Filtering in Artefact-based m-Learning Environments
Hoff, Christian; Gratz, Patrick UL; Rothkugel, Steffen UL

in International Workshop on Mobile and Ubiquitous Learning Environments (MULE 2007) held in conjunction with 15th International Conference on Computers in Education (ICCE 2007) (2007)

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See detailCrePes-Towards a Collaborative M-Learning Platform Leveraging Algorithmic Understanding in Schools
Gratz, Patrick UL; Rothkugel, Steffen UL; Scholtes, Ingo et al

in Proceedings of IADIS-2006: International Conference Mobile Learning 2006 (2006)

Detailed reference viewed: 16 (1 UL)