Fast context-aware recommendations with factorization machines

Steffen Rendle; Zeno Gantner; Christoph Freudenthaler; Lars Schmidt-Thieme

doi:10.1145/2009916.2010002

Abstract

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 context-aware 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 context-aware methods.

Keywords

GeneralityComputer scienceRecommender systemContext (archaeology)Categorical variableVariety (cybernetics)FactorizationMachine learningArtificial intelligenceContext modelAlgorithmObject (grammar)

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Publication Info

Year: 2011
Type: article
Pages: 635-644
Citations: 584
Access: Closed

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APA Style

                            
                                    Steffen Rendle, 
                                
                                    Zeno Gantner, 
                                
                                    Christoph Freudenthaler
                                
                                et al.
                            
                            (2011). 
                            Fast context-aware recommendations with factorization machines. 
                            
                            , 635-644.
                            https://doi.org/10.1145/2009916.2010002

Identifiers

DOI: 10.1145/2009916.2010002