[1]郭少成,陈松灿.稀疏化的因子分解机[J].智能系统学报,2017,12(6):816-822.[doi:10.11992/tis.201706030]
 GUO Shaocheng,CHEN Songcan.Sparsified factorization machine[J].CAAI Transactions on Intelligent Systems,2017,12(6):816-822.[doi:10.11992/tis.201706030]
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稀疏化的因子分解机

参考文献/References:
[1] RAO C R, TOUTENBURG H. Linear models[M]. New York: Springer, 1995: 3-18.
[2] ADOMAVICIUS G, TUZHILIN A. Context-aware recommender systems[M]. US: Springer, 2015: 191-226.
[3] RENDLE S. Factorization machines[C]//IEEE 10th International Conference on Data Mining. Sydney, Australia, 2010: 995-1000.
[4] RENDLE S. Learning recommender systems with adaptive regularization[C]//Proceedings of the fifth ACM international conference on Web search and data mining. Seattle, USA, 2012: 133-142.
[5] TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the royal statistical society, Series B (Methodological), 1996,73(3): 267-288.
[6] YUAN M, LIN Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2006, 68(1): 49-67.
[7] SIMON N, FRIEDMAN J, HASTIE T, et al. A sparse-group lasso[J]. Journal of computational and graphical statistics, 2013, 22(2): 231-245.
[8] BLONDEL M, FUJINO A, UEDA N, et al. Higher-order factorization machines[C]//Advances in Neural Information Processing Systems. Barcelona, Spain 2016: 3351-3359.
[9] LI M, LIU Z, SMOLA A J, et al. DiFacto: distributed factorization machines[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Francisco, USA, 2016: 377-386.
[10] CHIN W S, YUAN B, YANG M Y, et al. An efficient alternating newton method for learning factorization machines [R].NTU:NTU,2016.
[11] DUCHI J, SINGER Y. Efficient online and batch learning using forward backward splitting[J]. Journal of Machine Learning Research, 2009, 10(12): 2899-2934.
[12] RENDLE S. Factorization machines with libfm[J]. ACM transactions on intelligent systems and technolog, 2012, 3(3): 57.
[13] LIU J, YE J. Moreau-Yosida regularization for grouped tree structure learning[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2010: 1459-1467.
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备注/Memo

收稿日期:2017-06-09;改回日期:。
基金项目:国家自然科学基金项目(61472186).
作者简介:郭少成,男,1993年生,硕士研究生,主要研究方向为机器学习、模式识别;陈松灿,男,1962年生,教授,博士生导师,博士,中国人工智能学会机器学习专委会主任,CCF高级会员,主要研究方向为模式识别、机器学习、神经计算。在国际主流期刊和顶级会议上发表多篇学术论文并多次获奖。
通讯作者:陈松灿.E-mail:s.chen@nuaa.edu.cn.

更新日期/Last Update: 2018-01-03
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