[1] Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17, 6 (2005), 734-749.

[2] Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In International Conference on Machine Learning. 214-223.

[3] Jesus Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutierrez. 2013. Recommender systems survey. Knowledge-based systems 46 (2013), 109- 132.

[4] Tseng-Hung Chen, Yuan-Hong Liao, Ching-Yao Chuang, Wan-Ting Hsu, Jianlong Fu, and Min Sun. 2017. Show, Adapt and Tell: Adversarial Training of Crossdomain Image Captioner. arXiv preprint arXiv:1705.00930 (2017).

[5] Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 191-198.

[6] Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 39-46.

[7] Qiang Cui, Shu Wu, Qiang Liu, and Liang Wang. 2016. A Visual and Textual Recurrent Neural Network for Sequential Prediction. arXiv preprint arXiv:1611.06668 (2016).

[8] Robin Devooght and Hugues Bersini. 2016. Collaborative ltering with recurrent neural networks. arXiv preprint arXiv:1608.07400 (2016).

[9] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672-2680.

[10] Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 549-558.