ant-learn-recsys:推荐系统从入门到实战
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2021-03-11
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ant-learn-recsys-master.zip （85个子文件） 
ant-learn-recsys-master 
09. Tensorflow使用LR和GBDT和DNN实现银行营销二分类.ipynb 55KB 15. 推荐系统当前最流行的Embedding算法.ipynb 10KB
推荐系统系列.pptx 2.73MB 08. Python使用Faiss实现向量近邻搜索.ipynb 20KB 04. Python训练item2vec实现电影相关推荐.ipynb 32KB 05. Python使用SparkALS矩阵分解实现电影推荐.ipynb 29KB 02. 训练word2vec实现内容相似推荐.ipynb 35KB recsys_papers 
Collaborative Filtering for Implicit Feedback Datasets.pdf 165KB Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf 2.83MB Movies recommendation system using collaborative filtering and k-means .pdf 631KB BERT Pre-training of Deep Bidirectional Transformers for Language Understanding.pdf 757KB Wide & Deep Learning for Recommender Systems.pdf 400KB Deep Neural Networks for YouTube Recommendations.pdf 877KB Item2Vec- Neural Item Embedding for Collaborative FilteringItem2Vec- Neural Item Embedding for Collaborative Filtering.pdf 989KB DeepFM A Factorization-Machine based Neural Network for CTR Prediction.pdf 814KB Factorization Machines.pdf 187KB Next Item Recommendation with Self-Attention.pdf 2.16MB Practical Lessons from Predicting Clicks on Ads at Facebook.pdf 774KB Inductive Representation Learning on Large Graphs.pdf 1.04MB A-Quick-Guide-to-Recommendations-using-Redis.pdf 276KB Faiss A Survey of Product Quantization.pdf 434KB Matrix Factorization Techniques For Recommender Systems.pdf 1.48MB Item-Based Collaborative Filtering Recommendation Algorithms.pdf 244KB Amazon.com recommendations- item-to-item collaborative filtering.pdf 359KB Real-time Personalization using Embeddings for Search Ranking at Airbnb.pdf 9.74MB Deep Learning Recommendation Model for Personalization and Recommendation Systems.pdf 1.67MB Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations.pdf 1.01MB Multi-Interest Network with Dynamic Routing for Recommendation at Tmall.pdf 1.28MB 03. 使用腾讯开源Word2vec实现内容相似推荐.ipynb 26KB 06. Python实现基于标签的推荐系统.ipynb 21KB ant-recsys-wb app.py 3KB __pycache__ app.cpython-37.pyc 379B user_rating.py 280B resources ratings.csv 2.27MB movies.csv 473KB item_embedding.csv 784KB user_embedding.csv 1.23MB .idea misc.xml 360B vcs.xml 183B deployment.xml 487B modules.xml 278B .gitignore 38B ant-recsys-wb.iml 732B inspectionProfiles profiles_settings.xml 174B embedding_manager.py 2KB movie_info.py 419B 01. 推荐系统开发环境检测.ipynb 3KB 07. Tensorflow2实现推荐系统双塔DNN排序.ipynb 39KB datas movielens_uid_movieids.csv 240KB bank bank-full.csv 4.4MB crazyant_blog_articles_wordsegs.csv 843KB
crazyant_blog_articles.xlsx 22KB movielens_movie_embedding.csv 696KB crazyant_blog_articles_word2vec.csv 41KB
small_tencent_embedding.txt 20.68MB als sample_movielens_ratings.txt 32KB movielens_sparkals_item_embedding.csv 784KB crazyant_blog_articles_wordsegs.xlsx 1.22MB movielens_sparkals_user_embedding.csv 1.23MB ml-latest-small tensorflow_movie_datawithindex.csv 168KB README.txt 8KB tensorflow_user_datawithindex.csv 174KB tensorflow_user_embedding.csv 347KB ratings.csv 2.37MB tags.csv 116KB movies.csv 483KB tensorflow_movie_embedding.csv 191KB tensorflow_two_tower.h5 7.72MB links.csv 193KB ratings_1m.csv 20.59MB wp_posts.json 2.12MB ml-1m movies.dat 167KB README 5KB ratings.dat 23.45MB users.dat 131KB .ipynb_checkpoints 08. Python使用Faiss实现向量近邻搜索-checkpoint.ipynb 20KB 09. Tensorflow使用LR和GBDT和DNN实现银行营销二分类-checkpoint.ipynb 55KB 15. 推荐系统当前最流行的Embedding算法-checkpoint.ipynb 10KB 06. Python实现基于标签的推荐系统-checkpoint.ipynb 21KB 04. Python训练item2vec实现电影相关推荐-checkpoint.ipynb 32KB 05. Python使用SparkALS矩阵分解实现电影推荐-checkpoint.ipynb 29KB 07. Tensorflow2实现推荐系统双塔DNN排序-checkpoint.ipynb 39KB 03. 使用腾讯开源Word2vec实现内容相似推荐-checkpoint.ipynb 26KB 01. 推荐系统开发环境检测-checkpoint.ipynb 2KB README.md 85B
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