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推荐系统数据集的介绍, 下载, 处理, 用到一个写一个. BARS 提供了一套标准化流程, 非常好用. git clone https://github.com/openbenchmark/BARS.git 注: 在运行预处理脚本时, 请注意修改脚本中的文件路径是否匹配. CTR Criteo Cr 阅读全文
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Samuel D. and Chechik G. Distributional robustness loss for long-tail learning. In International Conference on Computer Vision (ICCV), 2021. 概 本文利用 Di 阅读全文
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Cui J., Zhong Z., Liu S., Yu B. and Jia J. Parametric contrastive learning. In International Conference on Computer Vision (ICCV), 2021. 概 一种特殊的 super 阅读全文
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Wang W., Lin X., Feng F., He X., Lin M. and Chua T. Causal representation learning for out-of-distribution recommendation. In International World Wide 阅读全文
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Zhang Y., Tan Y., Zhang M., Liu Y., Chua T. and Ma S. Catch the black sheep: unified framework for shilling attack detection based on fraudulent actio 阅读全文
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Zhu X. and Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, 2002. 概 本文通过将有标签数据传播给无标签数据 阅读全文
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Lin C., Chen S., Li H., Xiao Y., Li L. and Yang Q. Attacking recommender systems with augmented user profiles. In ACM International Conference on Info 阅读全文
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Zhang H., Li Y., Ding B. and Gao J. Practical data poisoning attack against next-item recommendation. International World Wide Web Conferences (WWW), 阅读全文
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He X., He Z., Du X. and Chua T. Adversarial personalized ranking for recommendation. In International ACM SIGIR Conference on Research and Development 阅读全文
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Xiao J., Ye H., He X., Zhang H., Wu F. and Chua T. Attentional factorization machines: learning the weight of feature interactions via attention netwo 阅读全文
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He X. and Chua T. Neural factorization machines for sparse predictive analytics. In International ACM SIGIR Conference on Research and Development in 阅读全文
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Guo H., Tang R., Ye Y., Li Z. and He X. DeepFM: a factorization-machine based neural network for CTR prediction. In International Joint Conference on 阅读全文
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[1] Dem\check{s}ar, J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research (JMLR). vol. 7, pp. 1-30, 阅读全文
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Chang J. Markov Chain. 符号说明 \(\mathcal{S} = \{1, 2, \cdots, N\}\), 状态空间; \(X\), 定义在状态空间 \(\mathcal{S}\) 之上的随机变量; \(\pi_0, \pi_0(i) := \mathbb{P}(X_0 = 阅读全文
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Wang R., Fu B., Fu G. and Wang M. Deep & cross network for ad click predictions. Proceedings of the ADKDD, 2017. 概 Wide & Deep 模型虽然强大, 但是其 wide 部分仍需要复 阅读全文
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Cheng H., et al. Wide & deep learning for recommender systems. Proceedings of the 1st workshop on deep learning for recommender systems, 2016. 概 谷歌提的推 阅读全文
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Li B., Wang Y., Singh A. and Vorobeychik Y. Data poisoning attacks on factorization-based collaborative filtering. In Advances in Neural Information P 阅读全文
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[1] Lin X., Zhen H., Li Z., Zhang Q. and Kwong S. Pareto multi-task learning. In Advances in Neural Information Processing Systems (NIPS), 2019. [2] F 阅读全文
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He X., Liao L., Zhang H., Nie L., Hu X. and Chua T. Neural collaborative filtering. In International World Wide Web Conference (WWW), 2017. 概 对用户和物品隐变 阅读全文
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Sener O. and Koltun V. Multi-task learning as multi-objective optimization. In Advances in Neural Information Processing Systems (NIPS), 2018. 概 本文提出的 阅读全文