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Displaying 1-4 out of 4 total
Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition
Social Network Analysis and Mining, International Conference on Advances in
By Hendrik Wermser, Achim Rettinger, Volker Tresp
Issue Date:July 2011
The task of recommending items, like movies, to users is a core feature of many social networks. Standard approaches either use item or user similarity to suggest the next items users might be interested in. Recently, multivariate models like matrix factor...
Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics
IEEE Intelligent Systems
By Davide Barbieri,Daniele Braga,Stefano Ceri,Emanuele Della Valle,Yi Huang,Volker Tresp,Achim Rettinger,Hendrik Wermser
Issue Date:November 2010
A combined approach of deductive and inductive reasoning can leverage the clear separation between the evolving (streaming) and static parts of online knowledge at conceptual and technological levels.
Hierarchical Bayesian Models for Collaborative Tagging Systems
Data Mining, IEEE International Conference on
By Markus Bundschus, Shipeng Yu, Volker Tresp, Achim Rettinger, Mathaeus Dejori, Hans-Peter Kriegel
Issue Date:December 2009
Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for d...
Intelligent exploration for genetic algorithms: using self-organizing maps in evolutionary computation
Found in: Proceedings of the 2005 conference on Genetic and evolutionary computation (GECCO '05)
By Achim Rettinger, Heni Ben Amor
Issue Date:June 2005
Exploration vs. exploitation is a well known issue in Evolutionary Algorithms. Accordingly, an unbalanced search can lead to premature convergence. GASOM, a novel Genetic Algorithm, addresses this problem by intelligent exploration techniques. The approach...
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