馒头and花卷

摘要： Wang C., Yu Y., Ma W., Zhang M., Chen C., Liu Y. and Ma S. Towards representation alignment and uniformity in collaborative filtering. In ACM SIGKDD C 阅读全文

摘要： Peng S., Sugiyama K. and Mine T. Less is more: reweighting important spectral graph features for recommendation. In International ACM SIGIR Conference 阅读全文

摘要： Tian C., Xie Y., Li Y., Yang N. and Zhao W. Learning to denoise unreliable interactions for graph collaborative filtering. In ACM SIGIR Conference on 阅读全文

摘要： Fouss F., Pirotte A., Renders J. M. and Saerens M. Random-walk computation of similarities between nodes of a graph, with application to collaborative 阅读全文

摘要： Yang J., Chen C., Wang C. and Tsai M. HOP-Rec: high-order proximity for implicit recommendation. In ACM Conference on Recommender Systems (RecSys), 20 阅读全文

摘要： van den Berg R. Kipf T. N. and Weling M. Graph convolutional matrix completion. In Proceedings of the ACM SIGKDD International Conference on Knowledge 阅读全文

摘要： Cho J., Mall U., Bala K. and Hariharan B. PiCIE: unsupervised semantic segmentation using invariance and equivariance in clustering. In IEEE Conferenc 阅读全文

摘要： Hamilton M., Zhang Z., Hariharan B., Snavely N., Freeman W. T. Unsupervised semantic segmentation by distilling feature correspondences. In Internatio 阅读全文

摘要： Wang X., Zhang R., Sun Y. and Qi J. Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Mach 阅读全文

摘要： Schnabel T., Swaminathan A., Singh A., Chandak N., Joachims T. Recommendations as treatments: debiasing learning and evaluation. In International Conf 阅读全文

摘要： Ge Y., Tan J., Zhu Y., Xia Y., Luo J., Liu S., Fu Z., Geng S., Li Z. and Zhang Y. Explainable fairness in recommendation. In International ACM SIGIR C 阅读全文

摘要： Ge Y., Liu S., Gao R., Xian Y., Li Y., Zhao X., Pei C., Pei C., Sun F., Ge J., Ou W. and Zhang Y. Towards long-term fairness in recommendation. In ACM 阅读全文

摘要： Celis L. E., Straszak D. and Vishnoi N. K. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840, 2017. 概 本文讨论在一种'强硬'的 Fairness 约束下, 如何 ( 阅读全文

摘要： Liang D., Krishnan R. G., Hoffman M. D. and Jebara T. Variational autoencoders for collaborative filtering. In International Conference on World Wide 阅读全文

摘要： Suresh Harini. A framework for understanding sources of harm throughout the machine learning life cycle. arXiv preprint arXiv:1901.10002, 2019. 概 本文介绍 阅读全文

摘要： 目录概符号说明Structural Causal Models and the Causal HierarchyStructural Causal Model (SCM)Causal HierarchyLayer1-SeeingLayer2-DoingLayer3–Imagining例子Expres 阅读全文

摘要： Song Y., Sohl-Dickstein J., Kingma D. P., Kumar A., Ermon S. and Poole B. Score-based generative modeling through stochastic differential equations. I 阅读全文

摘要： Shen Y., Wu Y., Zhang Y., Shan C., Zhang J., Letaief K. B. and Li D. How powerful is graph convolution for recommendation? In ACM International Confer 阅读全文

摘要： He X., Deng K., Wang X., Li Y., Zhang Y. and Wang M. LightGCN: simplifying and powering graph convolution network for recommendation. In International 阅读全文

摘要： Zhu Z., Kim J. and Nguyen T. Fairness among new items in cold start recommender systems. In International ACM SIGIR Conference on Research and Develop 阅读全文