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...

• Type: Journal
• Year: 2011
• Where: SIGIR
• Authors: Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme
• Keywords: Context Variables, Information Technology, Iterative Optimization, Model Complexity, SIGIR 2011