Sequential Recommendation with User Memory Networks简介
Sequential Recommendation with User Memory Networks Abstract 存在的推荐系统算法总是将用户历史记录embed成一个单一隐式表示，这可能会失去一些特征或物品的关联性。因此作者提出记忆增强的神经网络与协同过滤的思想进行整合从而作出推荐。通过借助外部的记忆矩阵，作者可以存储并更新用户的历史记录，这有效的提高了模型的表达能力。 Introduct
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相关文章
- 1. Memory Augmented Graph Neural Networks for Sequential Recommendation翻译
- 2. GNN:Session-based Recommendation with Graph Neural Networks简介
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- 4. 序列推荐模型梳理（Sequential Recommendation）
- 5. Visual Question Answering with Memory-Augmented Networks
- 6. Sequential Scenario-Specific Meta Learner for Online Recommendation简析
- 7. Next Item Recommendation with Self-Attention简介
- 8. 论文笔记Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
- 9. 2020上半年的sequential recommendation论文整理01——概述
- 10. Graph Neural Networks for Social Recommendation