文件名称：ant-learn-recsys:推荐系统从入门到实战
文件大小：59.42MB
文件格式：ZIP
更新时间：2024-03-31 08:36:48
系统开源
蚂蚁学习记录系统 推荐系统从入门到实战 微信公众号：Ant蚁学Python
【文件预览】：
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__()
--------user_rating.py(280B)
--------resources()
--------.idea()
--------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()
--------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()
--------movielens_sparkals_item_embedding.csv(784KB)
--------crazyant_blog_articles_wordsegs.xlsx(1.22MB)
--------movielens_sparkals_user_embedding.csv(1.23MB)
--------ml-latest-small()
--------wp_posts.json(2.12MB)
--------ml-1m()
----.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)