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Source code and data in paper "MDFEND: Multi-domain Fake News Detection (CIKM'21)"
ChinesePersonRelationGraph, person relationship extraction based on nlp methods.中文人物关系知识图谱项目,内容包括中文人物关系图谱构建,基于知识库的数据回标,基于远程监督与bootstrapping方法的人物关系抽取,基于知识图谱的知识问答等应用。
A curated list of Big data papers reading for anyone who are eager to learn!
List of papers, reports and links of materials on Big Data and related topics.
A curated list of awesome distributed systems books, papers, resources and shiny things.
📄 🇨🇳 📃 论文阅读笔记（分布式系统、虚拟化、机器学习）Papers Notebook (Distributed System, Virtualization, Machine Learning)
Recommendation engine based on contextual word embeddings
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial…
Coding exercises and Problem Sets for MITx: 6.00.1x Introduction to Computer Science and Programming Using Python, edX, Feb 2018
[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"
A PyTorch implementation of Graph Neural Networks for Social Recommendation (GraphRec)
A PyTorch implementation of Convolutional Sequence Embedding Recommendation Model (Caser)
Source code for AAAI 2020 paper "Multi-Component Graph Convolutional Collaborative Filtering"
Classic papers and resources on recommendation
Best Practices on Recommendation Systems
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
The awesome and classic papers in recommendation system!!! Good luck to every RecSys-learner!
A Python implementation of LightFM, a hybrid recommendation algorithm.
An implementation of a deep learning recommendation model (DLRM)
免费学代码系列：小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战
RSTutorials: A Curated List of Must-read Papers on Recommender System.
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.