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We have introduced factorization machines
ICDM, pp.995-1000, (2010)
In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized ...More
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- Support Vector Machines are one of the most popular predictors in machine learning and data mining.
- The factorization machine models all nested variable interactions, but uses a factorized parametrization instead of a dense parametrization like in SVMs. The authors show that the model equation of FMs can be computed in linear time and that it depends only on a linear number of parameters.
- The authors show that the model equation of FMs can be computed in linear time and that it depends only on a linear number of parameters
- This allows direct optimization and storage of model parameters without the need of storing any training data for prediction.
- The authors show that FMs subsume many of the most successful approaches for the task of collaborative filtering including biased MF, SVD++ , PITF  and FPMC 
- Support Vector Machines are one of the most popular predictors in machine learning and data mining
- We show that the only reason why standard Support Vector Machines predictors are not successful in these tasks is that they cannot learn reliable parameters (‘hyperplanes’) in complex kernel spaces under very sparse data
- We show that the model equation of Factorization Machine can be computed in linear time and that it depends only on a linear number of parameters
- In section V, we show how factorization machines using such feature vectors as input data are related to specialized state-of-the-art factorization models
- Factorization Machine have a closed model equation that can be computed in linear time
- We have introduced factorization machines
- The authors have introduced factorization machines.
- FMs bring together the generality of SVMs with the benefits of factorization models.
- In contrast to SVMs, (1) FMs are able to estimate parameters under huge sparsity, (2) the model equation is linear and depends only on the model parameters and (3) they can be optimized directly in the primal.
- The expressiveness of FMs is comparable to the one of polynomial SVMs. In contrast to tensor factorization models.
- D. Factorized Personalized Markov Chains (FPMC)
- Introduces Factorization Machines which are a new model class that combines the advantages of Support Vector Machines with factorization models
- Shows that the model equation of FMs can be calculated in linear time and FMs can be optimized directly
- Shows the relationship to SVMs and the advantages of FMs for parameter estimation in sparse settings
- Shows that FMs can mimic these models just by specifying the input data
- Shows that the only reason why standard SVM predictors are not successful in these tasks is that they cannot learn reliable parameters in complex kernel spaces under very sparse data
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