【论文理解】Graph Neural News Recommendation with Unsupervised Preference Disentanglement

时间 2021-01-02

标签 NLP 数据挖掘机器学习繁體版

原文原文链接

本片论文是2020中国入选ACL的论文之一，是北邮计算机学院图数据挖掘和机器学习实验室的石川老师等和微软亚洲研究院合作完成的项目，我简单拜读了一下，写一点自己的浅薄理解，如有不对还请指出！ 1. 基本要素新闻推荐核心：如何更好的了解用户和新闻的表征。 consider high- order connectivity underlying the user-news interactions：一

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1. 推荐系统论文：Neural News Recommendation with Attentive Multi-View Learning
2. 推荐系统论文笔记---Neural News Recommendation with Attentive Multi-View Learning
3. GNN:Session-based Recommendation with Graph Neural Networks简介
4. Graph Neural Networks for Social Recommendation
5. Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks论文理解
6. 【论文解读 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
7. Neural News Recommendation with Long- and Short-term User Representation（LSTUR)
8. 论文笔记——News Recommendation with Topic-Enriched Knowledge Graphs
9. 【SR-GNN】Session-based Recommendation with Graph Neural Networks详解
10. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

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