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A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

机译：推荐系统中跨域用户建模的多视图深度学习方法

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Ali Elkahky; Yang Song; Xiaodong He;
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年度 2015
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1. Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations [J] . Li Yakun, Ren Jiadong, Liu Jiaomin, Knowledge-Based Systems . 2021,第MAYa23期

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2. Exploring user movie interest space: A deep learning based dynamic recommendation model [J] . Gan Mingxin, Cui Hongfei Expert systems with applications . 2021,第Jula期

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3. A Maximum Margin Matrix Factorization based Transfer Learning Approach for Cross-Domain Recommendation [J] . Veeramachaneni Sowmini Devi, Pujari Arun K., Padmanabhan Vineet, Applied Soft Computing . 2019,第期

机译：基于跨域推荐的最大边缘矩阵分解方法
4. Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems [C] . Wenjing Fu, Zhaohui Peng, Senzhang Wang, AAAI Conference on Artificial Intelligence . 2019

机译：深度融合的审查和内容在跨域推荐系统中的冷启动用户
5. A model-based music recommendation system for individual users and implicit user groups. [D] . Hu, Yajie. 2014

机译：针对个人用户和隐式用户组的基于模型的音乐推荐系统。
6. A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea [O] . Jin-Hyeok Park, Jeong-Heum Baek, Sun Jin Sym, 2020

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7. DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain [O] . Ye Bi, Liqiang Song, Mengqiu Yao, 2020

机译：DCDIR：保险域中的冷启动用户深度跨域推荐系统

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

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