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KR-GCN: Knowledge-Aware Reasoning with Graph Convolution Network for Explainable Recommendation
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/icdm.2002.1183878openalex-snapshot
Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002
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Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06
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Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95
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Collaborative Knowledge Base Embedding for Recommender Systems
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Proceedings of the 26th International Conference on World Wide Web - WWW '17
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Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Recurrent knowledge graph embedding for effective recommendation
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Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
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Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation
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Next-Item Recommendation via Collaborative Filtering with Bidirectional Item Similarity
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Graph-based Regularization on Embedding Layers for Recommendation
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Neural Feature-aware Recommendation with Signed Hypergraph Convolutional Network
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A guided tour to approximate string matching
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.31openalex-snapshot
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
inferredcites →https://doi.org/10.18653/v1/d19-1openalex-snapshot
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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