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Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.
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2015
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2015
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2016
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R. He and J. McAuley, “VBPR: visual bayesian personalized ranking from implicit feedback,” in AAAI , 2016, pp. 144–150
2016
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J. Wen, X. Li, J. She, S. Park, and M. Cheung, “Visual background recommendation for dance performances using dancer-shared images,” in iThings , 2016, pp. 521–527
2016
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R. He and J. McAuley, “Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,” in WWW , 2016, pp. 507–517
2016
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F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in SIGKDD , 2016, pp. 353–362
2016
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Y. S. Rawat and M. S. Kankanhalli, “Contagnet: Exploiting user context for image tag recommendation,” in MM , 2016, pp. 1102–1106
2016
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2016
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2016
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2016
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2017
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J. Chen, H. Zhang, X. He, L. Nie, W. Liu, and T.-S. Chua, “Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention,” in SIGIR , 2017, pp. 335–344
2017
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F. Zhuang, D. Luo, N. J. Yuan, X. Xie, and Q. He, “Representation learning with pair-wise constraints for collaborative ranking,” in WSDM , 2017, pp. 567–575
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R. He, W.-C. Kang, and J. McAuley, “Translation-based recommendation,” in RecSys , 2017, pp. 161–169
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X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in WWW , 2017, pp. 173–182
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X. He and T.-S. Chua, “Neural factorization machines for sparse predictive analytics,” in SIGIR , 2017, pp. 355–364
2017
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2018
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2018
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2018
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