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目录概MotivationAlterRec代码 Li J., Han H., Chen Z., Shomer H., Jin W., Javari A. and Tang J. Enhancing ID and text fusion via alternative training in sess 阅读全文
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目录概统计角度论证实验论证代码 Woolridge D., Wilner S. and Glick M. An analysis of sequential recommendation datasets. PERSPECTIVES, 2021. Harper F. M. and Konstan J 阅读全文
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目录概符号/缩写说明Training detailsDatasetsE2E 下 MoRec 是否优于 IDRec?Regular settingWarm setting越好的 encoder 带来越好的推荐效果?TS versus E2E?总结代码 Yuan Z., Yuan F., Song Y. 阅读全文
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目录概符号说明Softmax lossBilateral Softmax loss (BSL)代码 Wu J., Chen J., Wu J., Shi W., Zhang J. and Wang X. BSL: Understanding and Improving Softmax Loss fo 阅读全文
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目录定义性质旋转矩阵的一般重参数化:正交矩阵的一般重参数化:代码 [1] Gallier J. Remarks on the Cayley Representation of Orthogonal Matrices and on Perturbing the Diagonal of a Matrix 阅读全文
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目录概符号说明DualGCN代码 Wang Q., Wei Y., Yin J., Wu J., Song X. and Nie L. DualGNN: Dual graph neural network for multimedia recommendation. IEEE Transaction 阅读全文
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目录概符号说明MGCNMotivationBehavior-Guided PurifierMulti-View Information EncoderBehavior-Aware FuserPredicitonOptimation代码 Yu P., Tan Z., Lu G. and Bao B. 阅读全文
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目录概FREEDOMMotivationFrozen Item-Item graphDenoising User-Item Bipartite GraphTwo Graphs for Learning代码 Zhou X. and Shen Z. A tale of two graphs: Freez 阅读全文
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目录概符号说明LATTICEModality-aware Latent Structure LearningCombining with Collaborative Filtering代码 Zhang J., Zhu Y., Liu Q., Wu S., Wang S. and wang L. Mi 阅读全文
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目录概符号说明KGATEmbedding LayerAttentive Embedding Propagation Layers代码 Wang X., He X., Cao Y., Liu M. and Chua T. KGAT: Knowledge graph attention network 阅读全文
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目录概TransE|H|R Bordes A., Usunier N., Garcia-Duran A., Weston J. and Yakhnenko O. Translating embeddings for modeling multi-relational data. NIPS, 2013 阅读全文
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目录概符号说明ULTRA (a method for Unified, Learnable, and TRAnsferable KG representations)Relation Graph ConstructionConditional Relation Representations代码 G 阅读全文
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目录概符号说明MotivationNBFNet代码 Zhu Z., Zhang Z., Xhonneux L. and Tang J. Neural Bellman-Ford networks: A general graph neural network framework for link pr 阅读全文
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目录概符号说明PA-GNN Yang Y., Liang Y. and Zhang M. PA-GNN: Parameter-adaptive graph neural networks. ICML workshop, 2022. 概 一个自适应学习 GNN layer weights 的方法. 符 阅读全文
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目录概符号说明GPR-GNN代码 Chien E., Peng J., Li P. and Milenkovic O. Adaptive universal generalized pagerank graph neural network. ICLR, 2021. 概 GPR-GNN 自适应地学习 阅读全文
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目录概符号说明Popular homophily measures理想的准则现有的 metrics 的分析 Platonov O., Kuznedelev D., Babenko A. and Prokhorenkova L. Characterizing graph datasets for no 阅读全文
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目录概符号说明Homophily metricsPost-aggregation node similarity matrix代码 Luan S., Hua C., Lu Q., Zhu J., Zhao M., Zhang S., Chang X. and Precup D. Revisiting 阅读全文
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目录概符号说明Empirical AnalysisSkeleton GraphNode FetchingGraph Condensation代码 Cao L., Deng H., Wang C., Chen L. and Yang Y. Graph-skeleton: ~1% nodes are s 阅读全文
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目录概符号说明MotivationROLAND训练策略Live-update Evaluation代码 You J., Du T. and Leskovec J. ROLAND: Graph learning framework for dynamic graphs. KDD, 2022. 概 dy 阅读全文
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目录概符号说明EvolveGCN代码 Pareja A., Domeniconi G., Chen J., Ma T., Suzumura T., Kanezashi H., Kaler T., Schardl T. B. and Leiserson C. E. EvolveGCN: Evolvin 阅读全文