Shuai Zhang
Lina Yao
Aixin Sun
Yi Tay
Skip Abstract Section
Abstract
With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.
- 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. Google Scholar
Digital Library
- Taleb Alashkar, Songyao Jiang, Shuyang Wang, and Yun Fu. 2017. Examples-rules guided deep neural network for makeup recommendation. In Proceedings of the AAAI. 941--947. https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14773 Google ScholarDigital Library
- Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. 2014. Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755 (2014).Google Scholar
- Bing Bai, Yushun Fan, Wei Tan, and Jia Zhang. 2017. DLTSR: A deep learning framework for recommendation of long-tail web services. IEEE Transactions on Services Computing (2017), 1--1.Google Scholar
- Trapit Bansal, David Belanger, and Andrew McCallum. 2016. Ask the gru: Multi-task learning for deep text recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. 107--114. Google ScholarDigital Library
- Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).Google Scholar
- Basiliyos Tilahun Betru, Charles Awono Onana, and Bernabe Batchakui. 2017. Deep learning methods on recommender system: A survey of state-of-the-art. International Journal of Computer Applications 162, 10 (Mar 2017).Google Scholar
- Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 4 (2002), 331--370. Google ScholarDigital Library
- Xiaoyan Cai, Junwei Han, and Libin Yang. 2018. Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In Proceedings of the AAAI. 5747--5754. https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16310Google Scholar
- S. Cao, N. Yang, and Z. Liu. 2017. Online news recommender based on stacked auto-encoder. In Proceedings of the ICIS. 721--726. https://ieeexplore.ieee.org/document/7960088Google Scholar
- Rose Catherine and William Cohen. 2017. Transnets: Learning to transform for recommendation. In Proceedings of the Recsys. 288--296. Google ScholarDigital Library
- Cheng Chen, Xiangwu Meng, Zhenghua Xu, and Thomas Lukasiewicz. 2017. Location-aware personalized news recommendation with deep semantic analysis. IEEE Access 5 (2017), 1624--1638.Google ScholarCross Ref
- Cen Chen, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li, and Minghui Qiu. Locally connected deep learning framework for industrial-scale recommender systems. In Proceedings of the WWW. 769--770. Google ScholarDigital Library
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention. In Proceedings of the SIGIR. ACM. Google ScholarDigital Library
- Minmin Chen, Zhixiang Xu, Kilian Weinberger, and Fei Sha. 2012. Marginalized denoising autoencoders for domain adaptation. arXiv preprint arXiv:1206.4683 (2012). Google ScholarDigital Library
- Shi-Yong Chen, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang, and Hai-Hong Tang. 2018. Stabilizing reinforcement learning in dynamic environment with application to online recommendation. In Proceedings of the SIGKDD. 1187--1196. Google ScholarDigital Library
- Xu Chen, Yongfeng Zhang, Qingyao Ai, Hongteng Xu, Junchi Yan, and Zheng Qin. 2017. Personalized key frame recommendation. In Proceedings of the SIGIR. 315--324. Google ScholarDigital Library
- Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Visually explainable recommendation. arXiv preprint arXiv:1801.10288 (2018).Google Scholar
- Yifan Chen and Maarten de Rijke. 2018. A collective variational autoencoder for top- recommendation with side information. arXiv preprint arXiv:1807.05730 (2018). Google ScholarDigital Library
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, and others. 2016. Wide 8 deep learning for recommender systems. In Proceedings of the Recsys. 7--10. Google ScholarDigital Library
- Sungwoon Choi, Heonseok Ha, Uiwon Hwang, Chanju Kim, Jung-Woo Ha, and Sungroh Yoon. 2018. Reinforcement learning based recommender system using biclustering technique. arXiv preprint arXiv:1801.05532 (2018).Google Scholar
- Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. End-to-end continuous speech recognition using attention-based recurrent NN: first results. arXiv preprint arXiv:1412.1602 (2014).Google Scholar
- Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Advances in Neural Information Processing Systems. Curran Associates, Inc., Montreal, 577--585. Google ScholarDigital Library
- Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, and Ed H. Chi. 2018. Q8R: A two-stage approach toward interactive recommendation. In Proceedings of the SIGKDD. 139--148. Google ScholarDigital Library
- Wei-Ta Chu and Ya-Lun Tsai. 2017. A hybrid recommendation system considering visual information for predicting favorite restaurants. WWWJ (2017), 1--19. Google ScholarDigital Library
- Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland. 160--167. Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for YouTube recommendations. In Proceedings of the Recsys. 191--198. Google ScholarDigital Library
- Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Deep coevolutionary network: Embedding user and item features for recommendation. arXiv preprint. arXiv preprint arXiv:1609.03675 (2016).Google Scholar
- Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Recurrent coevolutionary latent feature processes for continuous-time recommendation. In Proceedings of the Recsys. 29--34. Google ScholarDigital Library
- James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The Youtube video recommendation system. In Proceedings of the Recsys. 293--296. Google ScholarDigital Library
- Li Deng, Dong Yu, and others. 2014. Deep learning: Methods and applications. Foundations and Trends® in Signal Processing 7, 3--4 (2014), 197--387. Google ScholarDigital Library
- Shuiguang Deng, Longtao Huang, Guandong Xu, Xindong Wu, and Zhaohui Wu. 2017. On deep learning for trust-aware recommendations in social networks. IEEE Transactions on Neural Networks and Learning Systems 28, 5 (2017), 1164--1177.Google ScholarCross Ref
- Robin Devooght and Hugues Bersini. 2016. Collaborative filtering with recurrent neural networks. arXiv preprint arXiv:1608.07400 (2016).Google Scholar
- Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, and Fangxi Zhang. 2017. A hybrid collaborative filtering model with deep structure for recommender systems. In Proceedings of the AAAI. 1309--1315. https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14676 Google ScholarDigital Library
- Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In Proceedings of the Recsys. 152--160. Google ScholarDigital Library
- Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, and Bo Zhang. 2016. Collaborative filtering with user-item co-autoregressive models. arXiv preprint arXiv:1612.07146 (2016).Google Scholar
- Gintare Karolina Dziugaite and Daniel M. Roy. 2015. Neural network matrix factorization. arXiv preprint arXiv:1511.06443 (2015).Google Scholar
- Travis Ebesu and Yi Fang. 2017. Neural citation network for context-aware citation recommendation. In Proceedings of the SIGIR. ACM, Shinjuku, Tokyo, 1093--1096. Google ScholarDigital Library
- Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In WWW. 278--288. Google ScholarDigital Library
- Ignacio Fernández-Tobías, Iván Cantador, Marius Kaminskas, and Francesco Ricci. 2012. Cross-domain recommender systems: A survey of the state of the art. In Proceedings of the Spanish Conference on Information Retrieval. 24.Google Scholar
- Jianfeng Gao, Li Deng, Michael Gamon, Xiaodong He, and Patrick Pantel. 2014. Modeling interestingness with deep neural networks. (June 13 2014). US Patent App. 14/304,863.Google Scholar
- Kostadin Georgiev and Preslav Nakov. 2013. A non-iid framework for collaborative filtering with restricted boltzmann machines. In Proceedings of the ICML. 1148--1156. http://proceedings.mlr.press/v28/georgiev13.html Google ScholarDigital Library
- Carlos A Gomez-Uribe and Neil Hunt. 2016. The netflix recommender system: Algorithms, business value, and innovation. TMIS 6, 4 (2016), 13. Google ScholarDigital Library
- Yuyun Gong and Qi Zhang. 2016. Hashtag recommendation using attention-based convolutional neural network. In Proceedings of the IJCAI. 2782--2788. http://dl.acm.org/citation.cfm?id=3060832.3061010 Google ScholarDigital Library
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org. Google ScholarDigital Library
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the NIPS. 2672--2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf. Google ScholarDigital Library
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. In Proceedings of the IJCAI. 2782--2788. http://dl.acm.org/citation.cfm?id=3172077.3172127 Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Las Vegas, NV, 770--778.Google ScholarCross Ref
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the WWW. 507--517. Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. VBPR: Visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI. 144--150. http://dl.acm.org/citation.cfm?id=3015812.3015834 Google ScholarDigital Library
- Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, and Tat-Seng Chua. 2018. Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912 (2018). Google ScholarDigital Library
- Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In Proceedings of the SIGIR. 355--364. Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the WWW. 173--182. Google ScholarDigital Library
- Xiangnan He and Chua Tat-Seng. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the SIGIR. ACM, Shinjuku, Tokyo, 355--364. Google ScholarDigital Library
- Balázs Hidasi and Alexandros Karatzoglou. 2017. Recurrent neural networks with top-k gains for session-based recommendations. arXiv preprint arXiv:1706.03847 (2017).Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. Proceedings of the International Conference on Learning Representations. https://arxiv.org/abs/1511.06939Google Scholar
- Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the Recsys. 241--248. Google ScholarDigital Library
- Kurt Hornik. 1991. Approximation capabilities of multilayer feedforward networks. Neural Networks 4, 2 (1991), 251--257. Google ScholarDigital Library
- Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 5 (1989), 359--366. Google ScholarCross Ref
- Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In Proceedings of the WWW. 193--201. Google ScholarDigital Library
- Cheng-Kang Hsieh, Longqi Yang, Honghao Wei, Mor Naaman, and Deborah Estrin. 2016. Immersive recommendation: News and event recommendations using personal digital traces. In Proceedings of the WWW. 51--62. Google ScholarDigital Library
- Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In Proceedings of the SIGKDD. 1531--1540. Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the ICDM. 263--272. Google ScholarDigital Library
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the CVPR, Vol. 1. 3.Google Scholar
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the CIKM. 2333--2338. Google ScholarDigital Library
- Wenyi Huang, Zhaohui Wu, Liang Chen, Prasenjit Mitra, and C. Lee Giles. 2015. A neural probabilistic model for context based citation recommendation. In Proceedings of the AAAI. 2404--2410. http://dl.acm.org/citation.cfm?id=2886521.2886655 Google ScholarDigital Library
- Drew A. Hudson and Christopher D. Manning. 2018. Compositional attention networks for machine reasoning. arXiv preprint arXiv:1803.03067 (2018).Google Scholar
- Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the Recsys (RecSys'17). ACM, Como, 306--310. Google ScholarDigital Library
- Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010. Recommender Systems: An Introduction. Google ScholarDigital Library
- Yogesh Jhamb, Travis Ebesu, and Yi Fang. 2018. Attentive contextual denoising autoencoder for recommendation. (2018), 27--34. Google ScholarDigital Library
- X. Jia, X. Li, K. Li, V. Gopalakrishnan, G. Xun, and A. Zhang. 2016. Collaborative restricted Boltzmann machine for social event recommendation. In Proceedings of the ASONAM. 402--405. http://dl.acm.org/citation.cfm?id=3192424.3192498 Google ScholarDigital Library
- Xiaowei Jia, Aosen Wang, Xiaoyi Li, Guangxu Xun, Wenyao Xu, and Aidong Zhang. 2015. Multi-modal learning for video recommendation based on mobile application usage. In Proceedings of the 2015 IEEE International Conference on Big Data (Big Data). IEEE Computer Society, 837--842. Google ScholarDigital Library
- How Jing and Alexander J. Smola. 2017. Neural survival recommender. In Proceedings of the WSDM. ACM, 515--524. Google ScholarDigital Library
- Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. 2017. Cross domain recommender systems: A systematic literature review. ACM Computing Survyes 50, 3 (June 2017). Google ScholarDigital Library
- Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the Recsys. 233--240. Google ScholarDigital Library
- Donghyun Kim, Chanyoung Park, Jinoh Oh, and Hwanjo Yu. 2017. Deep hybrid recommender systems via exploiting document context and statistics of items. Information Sciences (2017), 72--87. Google ScholarDigital Library
- Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).Google Scholar
- Young-Jun Ko, Lucas Maystre, and Matthias Grossglauser. 2016. Collaborative recurrent neural networks for dynamic recommender systems. In Proceedings of the Asian Conference on Machine Learning. 366--381.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the SIGKDD. 426--434. Google ScholarDigital Library
- Yehuda Koren. 2010. Collaborative filtering with temporal dynamics. Communications of the ACM 53, 4 (2010), 89--97. Google ScholarDigital Library
- Hugo Larochelle and Iain Murray. 2011. The neural autoregressive distribution estimator. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 29--37.Google Scholar
- Hanbit Lee, Yeonchan Ahn, Haejun Lee, Seungdo Ha, and Sang-goo Lee. 2016. Quote recommendation in dialogue using deep neural network. In Proceedings of the SIGIR. 957--960. Google ScholarDigital Library
- Joonseok Lee, Sami Abu-El-Haija, Balakrishnan Varadarajan, and Apostol Paul Natsev. 2018. Collaborative deep metric learning for video understanding. In Proceedings of the KDD'18. ACM, 481--490. Google ScholarDigital Library
- Chenyi Lei, Dong Liu, Weiping Li, Zheng-Jun Zha, and Houqiang Li. 2016. Comparative deep learning of hybrid representations for image recommendations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2545--2553.Google ScholarCross Ref
- Jure Leskovec. 2015. New directions in recommender systems. In Proceedings of the WSDM. 3--4. https://dl.acm.org/citation.cfm?doid=2684822.2697044 Google ScholarDigital Library
- Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM, Raleigh, North Carolina, 661--670. Google ScholarDigital Library
- Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the SIGIR. ACM, London, 345--354. Google ScholarDigital Library
- Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep collaborative filtering via marginalized denoising auto-encoder. In Proceedings of the CIKM. ACM, Melbourne, 811--820. Google ScholarDigital Library
- Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the SIGKDD. 305--314. Google ScholarDigital Library
- Yang Li, Ting Liu, Jing Jiang, and Liang Zhang. Hashtag recommendation with topical attention-based LSTM. In Proceedings of the COLING. 943--952.Google Scholar
- Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the SIGKDD. 1734--1743. Google ScholarDigital Library
- Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. 2017. CCCFNet: A content-boosted collaborative filtering neural network for cross domain recommender systems. In Proceedings of the WWW. 817--818. Google ScholarDigital Library
- Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xDeepFM: Combining explicit and implicit feature interactions for recommender systems. arXiv preprint arXiv:1803.05170 (2018).Google Scholar
- Dawen Liang, Rahul G Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. arXiv preprint arXiv:1802.05814 (2018). Google ScholarDigital Library
- Dawen Liang, Minshu Zhan, and Daniel P. W. Ellis. 2015. Content-aware collaborative music recommendation using pre-trained neural networks. In Proceedings of the ISMIR. 295--301.Google Scholar
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI. AAAI Press, Austin, Texas, 2181--2187. Google ScholarDigital Library
- Zachary C. Lipton and Jacob Steinhardt. 2018. Troubling trends in machine learning scholarship. arXiv preprint arXiv:1807.03341 (2018).Google Scholar
- Juntao Liu and Caihua Wu. 2017. Deep Learning Based Recommendation: A Survey. Springer Singapore.Google Scholar
- Qiang Liu, Shu Wu, and Liang Wang. 2017. DeepStyle: Learning user preferences for visual recommendation. In Proceedings of the SIGIR. ACM, 841--844. Google ScholarDigital Library
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-term attention/memory priority model for session-based recommendation. In Proceedings of the SIGKDD. ACM, London, United Kingdom, 1831--1839. Google ScholarDigital Library
- Xiaomeng Liu, Yuanxin Ouyang, Wenge Rong, and Zhang Xiong. 2015. Item category aware conditional restricted boltzmann machine based recommendation. In Proceedings of the International Conference on Neural Information Processing. Springer-Verlag New York, Inc., Istanbul, 609--616. Google ScholarDigital Library
- Pablo Loyola, Chen Liu, and Yu Hirate. 2017. Modeling user session and intent with an attention-based encoder-decoder architecture. In Proceedings of the Recsys. ACM, Como, Italy, 147--151. Google ScholarDigital Library
- Pablo Loyola, Chen Liu, and Yu Hirate. 2017. Modeling user session and intent with an attention-based encoder-decoder architecture. In Proceedings of the Recsys. ACM, Como, Italy, 147--151. Google ScholarDigital Library
- Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015).Google Scholar
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the SIGIR. ACM, Santiago, 43--52. Google ScholarDigital Library
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, and others. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529.Google Scholar
- Isshu Munemasa, Yuta Tomomatsu, Kunioki Hayashi, and Tomohiro Takagi. Deep reinforcement learning for recommender systems. IEEE, 226--233.Google Scholar
- Cataldo Musto, Claudio Greco, Alessandro Suglia, and Giovanni Semeraro. 2016. Ask me any rating: A content-based recommender system based on recurrent neural networks. In Proceedings of the IIR.Google Scholar
- Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic. 2015. Deep learning applications and challenges in big data analytics. Journal of Big Data 2, 1 (2015), 1.Google ScholarCross Ref
- Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rego Drumond, and Lars Schmidt-Thieme. 2017. Personalized Deep Learning for Tag Recommendation. Springer International Publishing, 186--197.Google Scholar
- Xia Ning and George Karypis. 2010. Multi-task learning for recommender system. In Proceedings of 2nd Asian Conference on Machine Learning. 269--284.Google Scholar
- Wei Niu, James Caverlee, and Haokai Lu. 2018. Neural personalized ranking for image recommendation. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, Marina Del Rey, CA, 423--431. Google ScholarDigital Library
- Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. 2017. Embedding-based news recommendation for millions of users. In Proceedings of the SIGKDD. ACM, Halifax, NS, 1933--1942. Google ScholarDigital Library
- Yuanxin Ouyang, Wenqi Liu, Wenge Rong, and Zhang Xiong. 2014. Autoencoder-based collaborative filtering. In International Conference on Neural Information Processing. Springer International Publishing, 284--291.Google ScholarCross Ref
- Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the AAAI, Vol. 10. 230--235. Google ScholarDigital Library
- Yiteng Pana, Fazhi Hea, and Haiping Yua. 2017. Trust-aware collaborative denoising auto-encoder for top-n recommendation. arXiv preprint arXiv:1703.01760 (2017).Google Scholar
- Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. arXiv preprint arXiv:1802.08452 (2018).Google Scholar
- Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Recsys. ACM, Como, 130--137. Google ScholarDigital Library
- Yogesh Singh Rawat and Mohan S. Kankanhalli. 2016. ConTagNet: Exploiting user context for image tag recommendation. In Proceedings of the 2016 ACM on Multimedia Conference. ACM, Amsterdam, 1102--1106. Google ScholarDigital Library
- S. Rendle. 2010. Factorization machines. In 2010 IEEE International Conference on Data Mining. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. IEEE Computer Society, 452--461. Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender systems: Introduction and challenges. In Recommender Systems Handbook. Springer-Verlag, 1--34.Google ScholarDigital Library
- Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. 2011. Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the ICML. Omnipress, Bellevue, Washington, 833--840. Google ScholarDigital Library
- Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted boltzmann machines for collaborative filtering. In Proceedings of the ICML. 791--798. Google ScholarDigital Library
- Adam Santoro, David Raposo, David G. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap. 2017. A simple neural network module for relational reasoning. In Proceedings of the NIPS. 4967--4976. https://arxiv.org/abs/1706.01427 Google ScholarDigital Library
- Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the WWW. 111--112. Google ScholarDigital Library
- Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the Recsys. 297--305. Google ScholarDigital Library
- Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Representation learning of users and items for review rating prediction using attention-based convolutional neural network. In Proceedings of the MLRec.Google Scholar
- Joan Serrà and Alexandros Karatzoglou. 2017. Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks. In Proceedings of the Recsys. 279--287. Google ScholarDigital Library
- Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. 2016. Deep crossing: Web-scale modeling without manually crafted combinatorial features. In Proceedings of the SIGKDD. 255--262. Google ScholarDigital Library
- Xiaoxuan Shen, Baolin Yi, Zhaoli Zhang, Jiangbo Shu, and Hai Liu. 2016. Automatic recommendation technology for learning resources with convolutional neural network. In Proceedings of the International Symposium on Educational Technology. IEEE, Beijing, China, 30--34.Google ScholarCross Ref
- Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR) 47, 1 (2014), 3. Google ScholarDigital Library
- Elena Smirnova and Flavian Vasile. 2017. Contextual sequence modeling for recommendation with recurrent neural networks. In Proceedings of the DLRS. ACM, Como, 2--9. Google ScholarDigital Library
- Harold Soh, Scott Sanner, Madeleine White, and Greg Jamieson. 2017. Deep sequential recommendation for personalized adaptive user interfaces. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. 589--593. Google ScholarDigital Library
- Bo Song, Xin Yang, Yi Cao, and Congfu Xu. 2018. Neural collaborative ranking. arXiv preprint arXiv:1808.04957 (2018). Google ScholarDigital Library
- Yang Song, Ali Mamdouh Elkahky, and Xiaodong He. 2016. Multi-rate deep learning for temporal recommendation. In Proceedings of the SIGIR. 909--912. Google ScholarDigital Library
- Florian Strub, Romaric Gaudel, and Jérémie Mary. 2016. Hybrid recommender system based on autoencoders. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 11--16. Google ScholarDigital Library
- Florian Strub and Jeremie Mary. 2015. Collaborative filtering with stacked denoising autoencoders and sparse inputs. In Proceedings of the NIPS Workshop.Google Scholar
- Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009 (2009), 4. Google ScholarDigital Library
- Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2017. A deep architecture for content-based recommendations exploiting recurrent neural networks. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. ACM, Bratislava, 202--211. Google ScholarDigital Library
- Yosuke Suzuki and Tomonobu Ozaki. 2017. Stacked denoising autoencoder-based deep collaborative filtering using the change of similarity. In Proceedings of the WAINA. IEEE, Taipei, 498--502.Google ScholarCross Ref
- Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2016. A neural network approach to quote recommendation in writings. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, Indianapolis, Indiana, 65--74. Google ScholarDigital Library
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the Recsys. 17--22. Google ScholarDigital Library
- Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the WSDM. 565--573. Google ScholarDigital Library
- Jiaxi Tang and Ke Wang. 2018. Ranking distillation: Learning compact ranking models with high performance for recommender system. In Proceedings of the SIGKDD. 2289--2298. Google ScholarDigital Library
- Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Latent relational metric learning via memory-based attention for collaborative ranking. In Proceedings of the WWW. 729--739. Google ScholarDigital Library
- Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-pointer co-attention networks for recommendation. In Proceedings of the SIGKDD. 2309--2318. Google ScholarDigital Library
- Trieu H. Trinh, Andrew M. Dai, Thang Luong, and Quoc V. Le. 2018. Learning longer-term dependencies in rnns with auxiliary losses. arXiv preprint arXiv:1803.00144 (2018).Google Scholar
- Trinh Xuan Tuan and Tu Minh Phuong. 2017. 3D convolutional networks for session-based recommendation with content features. In Proceedings of the Recsys. 138--146. Google ScholarDigital Library
- Bartlomiej Twardowski. Modelling contextual information in session-aware recommender systems with neural networks. In Proceedings of the Recsys. 273--276. Google ScholarDigital Library
- Moshe Unger. 2015. Latent context-aware recommender systems. In Proceedings of the Recsys. 383--386. Google ScholarDigital Library
- Moshe Unger, Ariel Bar, Bracha Shapira, and Lior Rokach. 2016. Towards latent context-aware recommendation systems. Knowledge-Based Systems 104 (2016), 165--178. Google ScholarDigital Library
- Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle. 2016. Neural autoregressive distribution estimation. Journal of Machine Learning Research 17, 205 (2016), 1--37. Google ScholarDigital Library
- Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Proceedings of the NIPS. Curran Associates, Inc., 2643--2651. Google ScholarDigital Library
- Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. 2017. A meta-learning perspective on cold-start recommendations for items. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnet (Eds.). Curran Associates, Inc., 6904--6914. Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 5998--6008. Google ScholarDigital Library
- Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. 2017. DropoutNet: Addressing cold start in recommender systems. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 4957--4966. Google ScholarDigital Library
- Jeroen B. P. Vuurens, Martha Larson, and Arjen P. de Vries. Exploring deep space: Learning personalized ranking in a semantic space. In Proceedings of the Recsys. 23--28. Google ScholarDigital Library
- Hao Wang, Xingjian Shi, and Dit-Yan Yeung. 2015. Relational stacked denoising autoencoder for tag recommendation. In Proceedings of the AAAI, Boston, MA, USA. ACM, 3052--3058. Google ScholarDigital Library
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the SIGKDD. 1235--1244. Google ScholarDigital Library
- Hao Wang, SHI Xingjian, and Dit-Yan Yeung. 2016. Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks. In Proceedings of the NIPS. 415--423. http://dl.acm.org/citation.cfm?id=3157096.3157143 Google ScholarDigital Library
- Hao Wang and Dit-Yan Yeung. 2016. Towards Bayesian deep learning: A framework and some existing methods. TKDE 28, 12 (2016), 3395--3408. Google ScholarDigital Library
- Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. IRGAN: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the SIGIR. ACM, 515--524. Google ScholarDigital Library
- Lu Wang, Wei Zhang, Xiaofeng He, and Hongyuan Zha. 2018. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. In Proceedings of the SIGKDD. 2447--2456. Google ScholarDigital Library
- Qinyong Wang, Hongzhi Yin, Zhiting Hu, Defu Lian, Hao Wang, and Zi Huang. 2018. Neural memory streaming recommender networks with adversarial training. In Proceedings of the SIGKDD. 2467--2475. Google ScholarDigital Library
- Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu. 2017. What your images reveal: Exploiting visual contents for point-of-interest recommendation. In Proceedings of the WWW. 391--400. Google ScholarDigital Library
- Xiang Wang, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2017. Item silk road: Recommending items from information domains to social users. In Proceedings of the SIGIR. ACM, 185--194. Google ScholarDigital Library
- Xinxi Wang and Ye Wang. 2014. Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the MM. 627--636. Google ScholarDigital Library
- Xinxi Wang, Yi Wang, David Hsu, and Ye Wang. 2014. Exploration in interactive personalized music recommendation: A reinforcement learning approach. TOMM 11, 1 (2014), 7. Google ScholarDigital Library
- Xuejian Wang, Lantao Yu, Kan Ren, Guangyu Tao, Weinan Zhang, Yong Yu, and Jun Wang. 2017. Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In Proceedings of the SIGKDD. Google ScholarDigital Library
- Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. 2016. Collaborative filtering and deep learning based hybrid recommendation for cold start problem. In IEEE 14th International Conference on Dependable, Autonomic and Secure Computing. IEEE, 874--877.Google ScholarCross Ref
- Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. 2017. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications 69 (2017), 29--39.Google ScholarCross Ref
- Jiqing Wen, Xiaopeng Li, James She, Soochang Park, and Ming Cheung. 2016. Visual background recommendation for dance performances using dancer-shared images. IEEE, 521--527.Google Scholar
- Caihua Wu, Junwei Wang, Juntao Liu, and Wenyu Liu. 2016. Recurrent neural network based recommendation for time heterogeneous feedback. Knowledge-Based Systems 109 (2016), 90--103. Google ScholarDigital Library
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, and Alexander J. Smola. 2016. Joint training of ratings and reviews with recurrent recommender networks. ACM.Google Scholar
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent recommender networks. In Proceedings of the WSDM. 495--503. https://openreview.net/pdf?id=Bkv9FyHYx Google ScholarDigital Library
- Sai Wu, Weichao Ren, Chengchao Yu, Gang Chen, Dongxiang Zhang, and Jingbo Zhu. 2016. Personal recommendation using deep recurrent neural networks in NetEase. In Proceedings of the ICDE. 1218--1229.Google ScholarCross Ref
- Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the WSDM. 153--162. Google ScholarDigital Library
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617 (2017).Google Scholar
- Ruobing Xie, Zhiyuan Liu, Rui Yan, and Maosong Sun. 2016. Neural emoji recommendation in dialogue systems. arXiv preprint arXiv:1612.04609 (2016).Google Scholar
- Weizhu Xie, Yuanxin Ouyang, Jingshuai Ouyang, Wenge Rong, and Zhang Xiong. 2016. User occupation aware conditional restricted boltzmann machine based recommendation. IEEE, 454--461.Google Scholar
- Caiming Xiong, Victor Zhong, and Richard Socher. 2016. Dynamic coattention networks for question answering. arXiv preprint arXiv:1611.01604 (2016).Google Scholar
- Zhenghua Xu, Cheng Chen, Thomas Lukasiewicz, Yishu Miao, and Xiangwu Meng. 2016. Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling. In Proceedings of the CIKM. 1921--1924. Google ScholarDigital Library
- Zhenghua Xu, Thomas Lukasiewicz, Cheng Chen, Yishu Miao, and Xiangwu Meng. 2017. Tag-aware personalized recommendation using a hybrid deep model. IJCAI, 3196--3202. Google ScholarDigital Library
- Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the IJCAI. 3203--3209. http://dl.acm.org/citation.cfm?id=3172077.3172336 Google ScholarDigital Library
- Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, and Jiawei Han. Bridging collaborative filtering and semi-supervised learning: A neural approach for POI recommendation. In Proceedings of the SIGKDD. 1245--1254. http://dl.acm.org/citation.cfm?id=3172077.3172336 Google ScholarDigital Library
- Lina Yao, Quan Z. Sheng, Anne HH Ngu, and Xue Li. 2016. Things of interest recommendation by leveraging heterogeneous relations in the internet of things. ACM Transactions on Internet Technology (TOIT) 16, 2 (2016), 9. Google ScholarDigital Library
- Baolin Yi, Xiaoxuan Shen, Zhaoli Zhang, Jiangbo Shu, and Hai Liu. 2016. Expanded autoencoder recommendation framework and its application in movie recommendation. In Proceedings of the SKIMA. 298--303.Google ScholarCross Ref
- Haochao Ying, Liang Chen, Yuwen Xiong, and Jian Wu. 2016. Collaborative deep ranking: A hybrid pair-wise recommendation algorithm with implicit feedback. In Proceedings of the PAKDD. 555--567. Google ScholarDigital Library
- Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential recommender system based on hierarchical attention networks. In Proceedings of the IJCAI. 3926--3932. Google ScholarDigital Library
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. arXiv preprint arXiv:1806.01973 (2018). Google ScholarDigital Library
- Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, and Zheng Qin. 2018. Aesthetic-based clothing recommendation. In Proceedings of the WWW. 649--658. Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the SIGKDD. 353--362. Google ScholarDigital Library
- Qi Zhang, Jiawen Wang, Haoran Huang, Xuanjing Huang, and Yeyun Gong. Hashtag recommendation for multimodal microblog using co-attention network. IJCAI, 3420--3426. Google ScholarDigital Library
- Shuai Zhang, Yi Tay, Lina Yao, and Aixin Sun. 2018. Next item recommendation with self-attention. arXiv preprint arXiv:1808.06414 (2018).Google Scholar
- Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, and Manqing Dong. 2018. NeuRec: On nonlinear transformation for personalized ranking. arXiv preprint arXiv:1805.03002 (2018). Google ScholarDigital Library
- Shuai Zhang, Lina Yao, and Xiwei Xu. 2017. AutoSVD++: An efficient hybrid collaborative filtering model via contractive auto-encoders. ACM, 957--960. Google ScholarDigital Library
- Yongfeng Zhang, Qingyao Ai, Xu Chen, and W. Bruce Croft. 2017. Joint representation learning for top-n recommendation with heterogeneous information sources. In Proceedings of the CIKM. 1449--1458. Google ScholarDigital Library
- Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2018. Deep reinforcement learning for page-wise recommendations. arXiv preprint arXiv:1805.02343 (2018). Google ScholarDigital Library
- Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin. 2018. Recommendations with negative feedback via pairwise deep reinforcement learning. arXiv preprint arXiv:1802.06501 (2018). Google ScholarDigital Library
- Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the WWW. 167--176. Google ScholarDigital Library
- Lei Zheng, Chun-Ta Lu, Lifang He, Sihong Xie, Vahid Noroozi, He Huang, and Philip S Yu. 2018. MARS: Memory attention-aware recommender system. arXiv preprint arXiv:1805.07037 (2018).Google Scholar
- Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the WSDM. 425--434. Google ScholarDigital Library
- Yin Zheng, Cailiang Liu, Bangsheng Tang, and Hanning Zhou. 2016. Neural autoregressive collaborative filtering for implicit feedback. In Proceedings of the Recsys. 2--6. Google ScholarDigital Library
- Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A neural autoregressive approach to collaborative filtering. In Proceedings of the ICML. 764--773. https://dl.acm.org/citation.cfm?id=3045390.3045472 Google ScholarDigital Library
- Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2017. ATRank: An attention-based user behavior modeling framework for recommendation. arXiv preprint arXiv:1711.06632 (2017).Google Scholar
- Jiang Zhou, Cathal Gurrin, and Rami Albatal. 2016. Applying visual user interest profiles for recommendation 8 personalisation. Springer International Publishing, 361--366.Google Scholar
- Fuzhen Zhuang, Dan Luo, Nicholas Jing Yuan, Xing Xie, and Qing He. 2017. Representation learning with pair-wise constraints for collaborative ranking. In WSDM. 567--575. Google ScholarDigital Library
- Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, and Qing He. 2017. Representation learning via dual-autoencoder for recommendation. Neural Networks 90 (2017), 83--89.Google ScholarCross Ref
- Yi Zuo, Jiulin Zeng, Maoguo Gong, and Licheng Jiao. 2016. Tag-aware recommender systems based on deep neural networks. Neurocomputing 204 (2016), 51--60. Google ScholarDigital Library
Index Terms
- Deep Learning Based Recommender System: A Survey and New Perspectives
Recommendations
CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning
AbstractThe cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario ...
A survey of autoencoder-based recommender systems
AbstractIn the past decade, recommender systems have been widely used to provide users with personalized products and services. However, most traditional recommender systems are still facing a challenge in dealing with the huge volume, complexity, and ...
A deep reinforcement learning based long-term recommender system
AbstractRecommender systems aim to maximize the overall accuracy for long-term recommendations. However, most of the existing recommendation models adopt a static view, and ignore the fact that recommendation is a dynamic sequential decision-...
Highlights- A novel top-N interactive recommender system based on deep reinforcement learning is proposed.
Comments