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We present a new perspective on graph-based methods for collaborative ranking for recommender systems.
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V. Kalofolias, X. Bresson, M. Bronstein, and P. Vandergheynst, “Matrix completion on graphs,” in NIPS Workshop on “Out of the Box: Robustness in High Dimension” , 2014
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J. Lee, S. Bengio, S. Kim, G. Lebanon, and Y. Singer, “Local collaborative ranking,” in WWW ’14: Proc. of the 23rd Int. Conf. on World Wide Web , 2014, pp. 85–96
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J. Lee, S. Kim, G. Lebanon, and Y. Singer, “Local low-rank matrix approximation,” in ICML ’13: Proc. of the 30th Int. Conf. on Machine learning , 2013
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H. Yun, H.-F. Yu, C.-J. Hsieh, S. V. N. Vishwanathan, and I. Dhillon, “Nomad: Non-locking, stochastic multi-machine algorithm for asynchronous and decentralized matrix completion,” Proc. VLDB Endow. , vol. 7, no. 11, pp. 975–986, 2014
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X. Ning and G. Karypis, “Recent advances in recommender systems and future directions,” in Pattern Recognition and Machine Intelligence , ser. Lecture Notes in Computer Science. Springer, 2015, vol. 9124, pp. 3–9
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Z. Shi, J. Sun, and M. Tian, “Harmonic extension,” 2015, http://arxiv.org/abs/1509.06458
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S. Osher, Z. Shi, and W. Zhu, “Low dimensional manifold model for image processing,” UCLA, Tech. Rep. CAM report 16-04, 2016
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