论文信息 - SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder - 字舞流文

SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder

Hang Zheng | Xing Xing | Zhichun Jia | Jianyan Luo | Mindong Xin | Hang Zheng | Xing Xing | Zhichun Jia | Jianyan Luo | Mindong Xin

[1] Kai Chen,et al. Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..

[2] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.

[3] Robin D. Burke,et al. Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[4] Francesco Ricci,et al. Context-Aware Recommender Systems , 2011, AI Mag..

[5] Jae Kyeong Kim,et al. A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[6] Donghyun Kim,et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation , 2016, RecSys.

[7] Xing Zhao,et al. Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8] Jonathan L. Herlocker,et al. Evaluating collaborative filtering recommender systems , 2004, TOIS.

[9] Iván Cantador,et al. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols , 2013, User Modeling and User-Adapted Interaction.

[10] Paul Resnick,et al. Recommender systems , 1997, CACM.

[11] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.