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  • Knowledge Graph Convolutional Networks for Recommender Systems
    Convolutional Networks (KGCN) for recommender systems The key idea of KGCN is to aggregate and incorporate neighborhood in-formation with bias when calculating the representation of a given entity in the KG Such a design has two advantages: (1) Through the operation of neighborhood aggregation, the local proximity struc-
  • KGCN — RecBole 1. 2. 1 documentation
    For each item, aggregate the entity representation and its neighborhood representation into a single vector Parameters: user_embeddings (torch FloatTensor) – The embeddings of users, shape: [batch_size, embedding_size]
  • GitHub - hwwang55 KGCN: A tensorflow implementation of . . .
    KGCN is Knowledge Graph Convolutional Networks for recommender systems, which uses the technique of graph convolutional networks (GCN) to proces knowledge graphs for the purpose of recommendation
  • 【论文笔记】KGCN：知识图谱 + 图卷积神经网络的推荐系统 - 知乎
    原文提出一种融合 KG 特点与 图卷积神经网络 的模型（KGCN），也就是在计算 KG 中某一个给定的 entity 的表示时，将邻居信息与偏差一并结合进来。主要体现出如下的优势：
  • KGCN_基于知识图谱的推荐系统（KG+GCN） - CSDN博客
    今天，我们将深入探索一个在推荐系统领域内引发变革的开源项目——KGCN-pytorch，这是基于Pytorch实现的知识图谱卷积网络（KGCN）在推荐系统的创新尝试。
  • Neighborhood contrastive representation learning for . . .
    Neighborhood Contrast Module utilizes a region-level contrastive learning method to improve the quality of extracted node representation by maximizing the similarities of top K nearest neighbor nodes, i e , positive pairs, and minimizing the similarities of other nodes, i e , negative pairs
  • KGCN: Knowledge Graph Convolutional Networks for Recommender . . .
    KGCN은 모델 $\mathcal{F}$의 parameter $\boldsymbol{\Theta}$와 $Y,G$를 사용하여 임의의 유저 $u$와 item $v$간의 상호작용을 binary classification하도록 고안되었고 아래 식과 같이 쓸 수 있습니다 $$\hat{y}_{uv}=\mathcal{F}( u,v | \boldsymbol{\Theta},Y,G)$$





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