Incremental Graph Convolutional Network for Collaborative Filtering（阅读论文笔记）

In terms of data storage, IGCN does not require to store all the historical data to complete the training, but only stores a small amount of historical data. When taking interactions from a new time period, IGCN will update the stored historical data with the new data at the same time

文章综述(是我比较粗浅的理解)：

本文提出的，是一种新的基于神经网络的CF算法。
一方面，通过增量学习的方式，改进先前基于RNN的CF算法中，存在的“灾难性遗忘的问题”。
做到这一点，通过使用MAML、堆叠iTCN，从而提高模型从新数据中整合新知识和提炼已有知识的能力(可塑性)；

Q&A

为什么GNN能够提高CF算法的效率
First, the ability to leverage local neighborhood information among users and/or items
其次，可以灵活合并辅助信息，如社会信息、知识图等，可以帮助缓解协同过滤中众所周知的数据稀疏性问题。
本文中所说的subgraph：
newly observed user-item interactions can be regarded as a subgraph, which only contains the users and items (as nodes) and their interactions (as edges) over a short period of time, e.g., one week, one month.
实现的小细节：
It is worth noting that we set different

Incremental Graph Convolutional Network for Collaborative Filtering（阅读论文笔记）相关推荐
1. Neural Collaborative Filtering【论文笔记】
  文章目录 1 文章为了解决什么问题? 2 文章提出了什么方法? 3 模型架构以及原理? 3.1 推荐问题基本描述 3.2 矩阵分解(Matrix Factorization, MF) 3.3 NCF通 ...
2. 论文阅读06——《CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering》
  欢迎到我的个人博客看原文论文阅读06--<CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network f ...
3. 论文阅读”Multigraph Fusion for Dynamic Graph Convolutional Network“（TNNLS2022）
  论文标题 Multigraph Fusion for Dynamic Graph Convolutional Network 论文作者.链接作者: Gan, Jiangzhang and Hu, R ...
4. 图卷积网络 GCN Graph Convolutional Network（谱域GCN）的理解和详细推导
  文章目录 1. 为什么会出现图卷积神经网络? 2. 图卷积网络的两种理解方式 2.1 vertex domain(spatial domain):顶点域(空间域) 2.2 spectral domai ...
5. Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection阅读笔记
  Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection阅读笔记文章标题:A ...
6. 论文笔记（SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation）
  一个有效的基于图卷积神经网络的社交推荐模型原文链接:SocialGCN: An Efficient Graph Convolutional Network based Model for Socia ...
7. paper reading：Part-based Graph Convolutional Network for Action Recognition
  paper reading:Part-based Graph Convolutional Network for Action Recognition 文章目录 paper reading:Part- ...
8. 行人轨迹论文:STUGCN:A Social Spatio-Temporal Unifying Graph Convolutional Network for Trajectory Predictio
  STUGCN:A Social Spatio-Temporal Unifying Graph Convolutional Network for Trajectory Prediction用于轨迹预测 ...
9. RA-GCN：Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
  Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition TCSVT2020 ...
10. 多尺度动态图卷积神经网络----Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classificati
  一.摘要卷积神经网络(CNN)在表示高光谱图像和实现高光谱图像分类方面表现出令人印象深刻的能力.然而,传统的CNN模型只能对固定大小和权重的规则正方形图像区域进行卷积,因此不能普遍适用于具有不同对象 ...
最新文章
热门文章

Incremental Graph Convolutional Network for Collaborative Filtering（阅读论文笔记）

Incremental Graph Convolutional Network for Collaborative Filtering（阅读论文笔记）

文章综述(是我比较粗浅的理解)：

Q&A

Incremental Graph Convolutional Network for Collaborative Filtering（阅读论文笔记）相关推荐

最新文章

热门文章