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SIGIR
2011
ACM

Fast context-aware recommendations with factorization machines

7 years 9 months ago
Fast context-aware recommendations with factorization machines
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for contextaware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of contextaware methods. We propose to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions. This approach results in fast contextaware recommendations because the model equation of FMs can be computed in linear time both in the number of context var...
Steffen Rendle, Zeno Gantner, Christoph Freudentha
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGIR
Authors Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme
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