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Software / MyMedia Dynamic Personalization and Recommendation Framework

The MyMedia toolkit is an extensible software framework for multimedia recommendation, incorporating cutting edge recommender algorithms and metadata enrichment. This C# developed research project makes it easier to study, develop and deploy personalization technology.

The MyMedia software toolkit is the outcome of an international collaboration to develop an extensible software framework for multimedia recommendation, incorporating cutting edge recommender algorithms, metadata enrichment, and software design. It will be tested in field trials under realistic conditions while being made available to the research community. This software provides a structured approach to personalization, as well as reusable reference implementations of popular recommendation and enrichment algorithms.

More information about the software can be found at http://mymediaproject.codeplex.com/

See also: MyMediaLite, a stripped-down open source/free software library containing the algorithms of the MyMedia Framework


The MyMedia toolkit has benefited from the contributions of many people involved in the MyMedia project through its partners: EMIC; BT; the BBC; the Technical University of Eindhoven; the University of Hildesheim, Microgenesis and Novay (formerly the Telematica Instituut). The MyMedia software toolkit has been developed as part of the MyMedia project funded through the EU Framework 7 Programme Networked Media initiative (Project Number IST-2008-215006).

At ISMLL: Steffen Rendle, Christoph Freudenthaler, Zeno Gantner


Please check the Codeplex website for more recent versions of the MyMedia Framework.


Installation instructions can be found in the .zip files.

Related publications:

  • Zeno Gantner, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme (2009):
    Optimal Ranking for Video Recommendation, in User Centric Media: First International Conference, UCMedia 2009, Revised Selected Papers, Springer. PDF
  • Paul Marrow, Rich Hanbidge, Steffen Rendle, Christian Wartena, Christoph Freudenthaler (2009):
    MyMedia: Producing an Extensible Framework for Recommendation, in Networked Electronic Media Summit 2009.
  • Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme (2009):
    BPR: Bayesian Personalized Ranking from Implicit Feedback, in Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). PDF
  • Steffen Rendle, Lars Schmidt-Thieme (2008):
    Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems, in Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys 2008), ACM. PDF