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目录概符号说明MetricsSampled-based ranking例子Sampled metrics Krichene W. and Rendle S. On sampled metrics for item recommendation. KDD, 2020. 概 作者对推荐系统中 sampl 阅读全文
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Page L., Brin S., Motwani R. and Winograd T. The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab, 1998. 概 经典的 阅读全文
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Hamilton W. L., Ying R. and Leskovec J. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (NIPS) 阅读全文
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Wang Y., Li C., Li M., Jin W., Liu Y., Sun H., Xie X. and Tang J. Localized graph collaborative filtering. 概 现在的推荐系统, 倾向于为每个 user, item 构建 embeddings. 阅读全文
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Rong Y., Huang W., Xu T. and Huang J. DropEdge: towards deep graph convolutional networks on node classification. In International Conference on Learn 阅读全文
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Gori M. and Pucci A. ItemRank: a random-walk based scoring algorithm for recommender engines. In International Joint Conferences on Artificial Intelli 阅读全文
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data 此部分模块包含了一些推荐系统数据集的定义以及预处理方法. datasets RecDataSet freerec.data.RecDataSet 提供了一般 (未处理) 的数据集的框架, 它的子类必须提供: _cfg 的类属性, 其中定义了数据集的 Fields; raw2data 方法, 阅读全文
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Ding S., Wu P., Feng F., Wang Y., He X., Liao Y. and Zhang Y. Addressing unmeasured confounder for recommendation with sensitivity analysis. In ACM SI 阅读全文
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Chen J., Dong H., Qiu Y., He X., Xin X., Chen L., Lin G. and Yang K. AutoDebias: learning to debias for recommendation. In International ACM SIGIR Con 阅读全文
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Frankle J. and Carbin M. The lottery ticket hypothesis: finding sparse, trainable neural networks. In International Conference on Learning Representat 阅读全文
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Guo S., Zou L., Liu Y., Ye W., Cheng S., Wang S., Chen H., Yin D. and Chang Y. Enhanced doubly robust learning for debiasing post-click conversion rat 阅读全文
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Liu C., Gao C., Jin D. and Li Y. Improving location recommendation with urban knowledge graph. In ACM SIGKDD International Conference on Knowledge Dis 阅读全文
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Defferrard M., Bresson X. and Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural I 阅读全文
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Cao J., Lin X., Cong X., Ya J., Liu T. and Wang B. DisenCDR: learning disentangled representations for cross-domain recommendation. In ACM SIGIR Confe 阅读全文
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Poole B., Ozair S., van den Oord A., Alemi A. A. and Tucker G. On variational bounds of mutual information. In International Conference on Machine Lea 阅读全文
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Le T. and Lauw H. W. Explainable recommendation with comparative constraints on product aspects. In ACM International Conference on Web Search and Dat 阅读全文
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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
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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
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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
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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