Hybreed Framework

Hybreed is a software framework for building complex context-aware applications including a set of components for developing recommender systems. It is based on a concept for processing context that we call Dynamic Contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors e.g. derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the operational, interpretation-based nature of context.

As an extension of the generic framework, Hybreed RecViews contains components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that produce recommendations for both individual users and groups. The systems includes a range of recommendation algorithms, strategies for producing group recommendations, and templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner while still offering intuitive and extensible methods for the developer.


  • Independent development


  • Java


Werner Gaulke


Further contributors

Tim Hussein


Timm Linder



Hybreed: A Software Framework for Developing Context-Aware Hybrid Recommender Systems

Hussein, T., Linder, T., Gaulke, W., and Ziegler, J. (2012). User modeling and user adapted interaction.

Situationsgerechtes Recommending – Kontextadaptive, hybride Empfehlungsgenerierung

Hussein, T. and Ziegler, J. (2011). Informatik Spektrum, 34(2), 143–152.

Hybride, kontext-sensitive Generierung von Produktempfehlungen

Hussein, T. and Gaulke, W. (2010). i-com – Zeitschrift für interaktive und kooperative Medien, 9(2), 16–23.