## AI helps you reading Science

## AI Insight

AI extracts a summary of this paper

Weibo:

# Factorization Machines

ICDM, pp.995-1000, (2010)

EI

Keywords

Abstract

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

Code:

Data:

Introduction

- 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++ [2], PITF [3] and FPMC [4]

Highlights

- 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

Conclusion

- 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)

Funding

- 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

Reference

- R. A. Harshman, “Foundations of the parafac procedure: models and conditions for an ’exploratory’ multimodal factor analysis.” UCLA Working Papers in Phonetics, pp. 1–84, 1970.
- Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in KDD ’08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2008, pp. 426–434.
- S. Rendle and L. Schmidt-Thieme, “Pairwise interaction tensor factorization for personalized tag recommendation,” in WSDM ’10: Proceedings of the third ACM international conference on Web search and data mining. New York, NY, USA: ACM, 2010, pp. 81–90.
- S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in WWW ’10: Proceedings of the 19th international conference on World wide web. New York, NY, USA: ACM, 2010, pp. 811–820.
- T. Joachims, “Optimizing search engines using clickthrough data,” in KDD ’02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2002, pp. 133–142.
- V. N. Vapnik, The nature of statistical learning theory. New York, NY, terms that do not depend on i vanish and the FM model USA: Springer-Verlag New York, Inc., 1995.
- N. Srebro, J. D. M. Rennie, and T. S. Jaakola, “Maximum-margin matrix equation is equivalent to: factorization,” in Advances in Neural Information Processing Systems 1MIT Press, 2005, pp. 1329–1336.
- R. Salakhutdinov and A. Mnih, “Bayesian probabilistic matrix factorization using Markov chain Monte Carlo,” in Proceedings of the International Conference on Machine Learning, vol. 25, 200

Tags

Comments

数据免责声明

页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果，我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问，可以通过电子邮件方式联系我们：report@aminer.cn