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Name: Scarlett•S
Type: User
Name: Scarlett•S
Type: User
Real time news scraping and recommendation system
Tools for downloading and analyzing summaries and evaluating summarization systems. https://summari.es/
News Recommendation System
Exploring algorithms in the domain of offline reinforcement learning (REM, Ensemble-DQN, DQN, ...)
An Open-Source Package for Knowledge Embedding (KE)
🍣 The one-stop shop for official Umi plugins and presets.
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
基于知识图谱的推荐系统
Pytorch implementation of the Attention-based Graph Neural Network(AGNN)
a news recommendation system based on user preference
The Rabin fingerprinting scheme is a method for implementing fingerprints using polynomials over a finite field
:dog: :clap: :star2: Welcome to star
Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
Best Practices on Recommendation Systems
Knowledge Base Embedding
A tensorflow implementation of RippleNet
Our implementation of the paper "Embedding-based News Recommendation for Millions of Users"
天眼查爬虫,selenium
From the Kindle Store Reviews on Amazon, sentiment analysis and book recommendation. Used Keras, FastText from Torch, and BERT. For recommender systems; SVDS, cosine-similarity, and solved the cold-start problem.
项目基于python3+mysql进行搭建的一个web爬虫,主要用于爬取天眼查的企业数据~(请勿用于商业用途,3q | 觉得有用,欢迎点个star)
tensorflow实战练习,包括强化学习、推荐系统、nlp等
天眼查爬虫
天眼查关键词爬取,selenium爬取,selenium滑块认证破解
天眼查 网站爬取 scrapy
reimplement TransE Mode
Reimplement TransH mode
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An Open Source Machine Learning Framework for Everyone
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
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Some thing interesting about web. New door for the world.
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Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.