Memory Augmented Graph Neural Networks for Sequential Recommendation翻译
论文下载地址 摘要 关键词：GNN、记忆网络、双线性函数 在许多推荐系统中，用户-物品交互的时间顺序可以反映用户行为的时间演化和顺序。 用户将与之交互的项可能取决于过去访问的项。然而，随着用户和项目的大量增加，序列推荐系统仍然面临着不小的挑战 :(1)短期用户兴趣建模的难度; :(2)获取长期用户兴趣的难度; (3)项目共现模式的有效建模。 为了解决这些挑战，我们提出了一个内存增强图神经网络(MA
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相关文章
- 1. Graph Neural Networks for Social Recommendation
- 2. Sequential Recommendation with User Memory Networks简介
- 3. Visual Question Answering with Memory-Augmented Networks
- 4. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻译
- 5. Introduction to Graph Neural Network翻译-第四章Vanilla Graph Neural Networks
- 6. GNN:Session-based Recommendation with Graph Neural Networks简介
- 7. Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
- 8. Attention and Augmented Recurrent Neural Networks
- 9. 论文分享 - Reasoning with Memory Augmented Neural Networks for Language Comprehension
- 10. Strategies For Pre-Training Graph Neural Networks