LeslieZhoa / My_Recommendation_System
Recommendation System based on python
☆13Updated 5 years ago
Alternatives and similar repositories for My_Recommendation_System:
Users that are interested in My_Recommendation_System are comparing it to the libraries listed below
- 推荐系统相关模型 包括召回和排序☆30Updated 5 years ago
- 简单的实现推荐系统的召回模型和排序模型,其中召回模型使用协同过滤算法,排序模型使用gbdt+lr算法☆59Updated 6 years ago
- ‘京东杯’《用户对品类下的购买预测》-冠军团队‘AI你所想’解决方案☆25Updated 5 years ago
- 基于用户行为的推荐算法大赛---第四名(临兵斗列)☆41Updated 8 years ago
- 推荐算法专题总结相关代码!☆18Updated 2 years ago
- 《推荐系统开发实战》代码及勘误☆59Updated 5 years ago
- 一个基于 fasttext + faiss 的商品内容相关推荐实现,nginx+uwsgi+flask / gunicorn+uvicorn+fastapi 提供api查询接口,增加Spark实现 Ansj+Word2vec+LSH+Phoenix☆51Updated last year
- 京东JDATA2019-用户对品类下店铺的购买预测☆18Updated 5 years ago
- CIKM 2019 E-Commerce AI Challenge - 超大规模推荐之用户兴趣高效检索☆11Updated 3 years ago
- 第二届阿里巴巴大数据智能云上编程大赛冠军解决方案☆31Updated 5 years ago
- CTR prediction models based on spark(LR,FM、XGBoost、XGBoostLR、XGBoostFM)☆35Updated 4 years ago
- 短视频内容理解与推荐竞赛☆83Updated 4 years ago
- 计算广告学习笔记☆24Updated 3 years ago
- DataFountain第五届达观 杯第4名方案☆12Updated 3 years ago
- 阿里巴巴ESMM模型解读☆41Updated 4 years ago
- 人工智能工程师直通车第三期 实战项目:广告点击率预测(CTR)。预测用户浏览给定网页的广告点击率,提高广告投放精准度。☆22Updated 6 years ago
- RecommenderSystems: from 0 to practice. 包括推荐系统实践和深度推荐系统两部分☆17Updated 3 years ago
- Impementation paper "Deep Neural Networks for YouTube Recommendations"☆76Updated 4 years ago
- 京东杯 2019 第六届泰达创新创业挑战赛-用户对品类下店铺购买预测_季军方案☆18Updated 5 years ago
- 短视频内容理解与推荐竞赛☆11Updated 6 years ago
- 2019年知乎看山杯专家发现算法大赛第六名完整解决方案☆39Updated 5 years ago
- IJCAI-18 阿里妈妈搜索广告转化预测大赛,top50方案☆13Updated 6 years ago
- 京东2019-用户对品类下店铺的购买预测(A榜单人13,B榜团队第7)☆19Updated 5 years ago
- ☆15Updated 6 years ago
- graph embedding spark implementation, include deepWalk, Node2Vec etc☆24Updated 4 years ago
- 数据挖掘18大算法实现以及其他相关经典DM算法☆12Updated 9 years ago
- rater, recommender systems. 推荐模型,包括:DeepFM,Wide&Deep,DIN,DeepWalk,Node2Vec等模型实现,开箱即用。☆44Updated 4 years ago
- 讯飞移动广告反欺诈算法竞赛☆31Updated 5 years ago
- 2017“达观杯”个性化推荐算法挑战赛-rank6☆43Updated 5 years ago
- Python 3.6 下的推荐算法解析,尽量使用简单的语言剖析原理,相似度度量、协同过滤、矩阵分解等☆104Updated 6 years ago