In this work, we have proposed a new model for tag-aware recommendation, namelyGraphIntentionEmbeddingNeuralNetwork (GIENN), to address these two issues. Specifically, the proposed approach GIENN initially cons
跨域推荐(cross-domain recommendation) 跨域推荐会充分利用域和域之间的相关关系,可以缓解冷启动与数据稀疏问题。根据信息在域之间的流动方向,又可以把跨域推荐进一步分为从源域到目标域的single-target CDR(STCDR), 源域与目标域相互影响的dual-target CDR(DTCDR),和DTCDR的推广multi-target CDR(MTCDR)。 多行为...
大多数基于图(Graph-based)的推荐都是利用positive edges/feedback来建模用户的偏好,但是缺忽视了negative edges/feedback,这些缺失的数据在一些现实场景中是广泛存在的。 发现1:现有的图神经网络都不是很适合建模负反馈信号,因为这是一种UI图上的高频信号。(这里为什么说不适合呢,因为GNNs的使用前提是,两个直接相连...
Graph Neural Networks for Social Recommendation 技术标签: GNN 深度学习 pytorch 神经网络Graph Neural Networks for Social Recommendation LINK: https://arxiv.org/abs/1902.07243 CLASSIFICATION: RECOMMENDER-SYSTEM, HETEROGENEOUS NETWORK, GCN YEAR: Submitted on 19 Feb 2019 (v1), last revised ... 查看...
"A Neural Influence Diffusion Model for Social Recommendation." SIGIR 2019. Session-based Recommendation [1] Wu, Shu, et al. "Session-Based Recommendation with Graph Neural Network." AAAI 2019. Application [1] Ying, Rex, et al. "Graph Convolutional Neural Networks for Web-Scale Recommender ...
DiffNet++ [DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation] 在一个统一的框架下建模影响力扩散和兴趣扩散。对于用户节点,首先利用GAT在二部图和神经网络上聚合邻居信息,注意力机制被用来混合邻居的两种表示,用户节点通过与混合向量相加来更新。对于item节点,利用GAT传播交互邻居...
2.2 Graph Neural Networks 三、Challenges of applying GNNs 3.1 Graph Construcion 3.2 Network Design 3.3 Model Optimization 3.4 Computation Efficiency 四、Existing Mthods ...
Here we present a hardware–software co-design to address these challenges, by designing an echo state graph neural network based on random resistive memory arrays, which are built from low-cost, nanoscale and stackable resistors for efficient in-memory computing. This approach leverages the ...
Graph Neural Networks for Social Recommendation 1. 摘要 构建基于图神经网络的推荐系统的三大挑战 the user-item graph encodes both interactions and their associated opinions social relations have heterogeneous strengths users involve in two graphs (e.g., the user-user social graph and the user-item ...
Furthermore, we can find that recommendation methods based on graph neural networks have achieved comparable success. In this study, we therefore propose a novel graph neural network to address the issue of preference shift for sequence recommendations in MOOCs. 3. Proposed framework In this section...