所以,今天看到了一篇从图学习方法视角切入的综述文章《Graph Learning Approaches to Recommender Systems: A Review》,自然而然的想跟大家分享了。 还是那句话,综述不仅起到索引的作用,更大的作用是给我们小白一个牛人视角中的知识体系,然后通过借阅牛人的综述来Fine Tune自己的知识网络,以此来丰富自己的知识库。 1...
5.2 Graph Representation Learning Approach 图表示学习就是将图上的节点映射到一个低维空间中。GRLRS可以分为三类。 5.2.1 Graph Factorization Machine based RS (GFMRS) GFMRS利用分解机分解图上的元路径(meta-path,链接两个实体的路径)通勤矩阵(commuting matrix)从而获得每个节点的潜在表示。这些潜在表示一般被用...
广义地说,在过去的几十年中,推荐系统中的主流建模范式已经从邻域方法(neighborhood methods)发展到基于表征学习(representation learning )的框架。 基于item的邻域方法直接向用户推荐他们历史交互过的items相似的items。通过直接使用用户的历史交互items来表示用户的偏好。 基于表征学习的方法,其将users和items两者编码为连续...
Graph learning based recommender systems: a reviewdoi:10.24963/IJCAI.2021/630Shoujin WangLiang HuYan WangXiangnan HeQuan Z. ShengMehmet A. OrgunLongbing CaoFrancesco RicciPhilip S. YuInternational Joint Conferences on Artificial Intelligence Organization...
This repository provides a summary of our research on Recommender Systems. It includes our code base on different recommendation topics, a comprehensive reading list and a set of bechmark data sets. Code Base Currently, we are interested in sequential recommendation, feature-based recommendation and ...
论文题目:MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems 论文链接:http://keg.cs.tsinghua.edu.cn/jietang/publications/KDD21-Huang-et-al-MixGCF.pdf 论文代码:https://github.com/huangtinglin/MixGCF
Recommender systems Collaborative filtering Knowledge graph Neural collaborative filtering Knowledge graph representation learning 1. Introduction With the advancement of internet and communication technologies, people are facing increase in data. Obtaining useful information from these big data is one of the ...
3.节点表示容易收到噪声交互的影响。在本文中作者通过在用户-物品图上引入自监督学习来改善GCN在推荐系统上的准确率和鲁棒性,将其称为Self-supervised Graph Learning(SGL),并应用在LightGCN模型上。SGL是模型无关的,并通过辅助自监督任务来补充监督任务中的信息以达成上述目的。
PinSage在训练的过程中采用了Multi-GPU形式,minibatch取值为512-4096不等,大的batchsize可能会导致收敛困难,论文采取了warmup策略,即根据线性规则在第一个epoch中逐步将学习率(learning rate)从一个小值增加到峰值,之后在指数级减小learning rate。在整个训练过程中使用“hard”负样本会使训练收敛所需的时间增加一倍。
Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016: 353-362.[3] Zhang Y, Ai Q, Chen X, et al. Learning over knowledge-base embeddings for recommendation[J]. arXiv preprint ...