ch3nboyu / RecSys_Course_deepshareLinks
recommendation system practice course from deepshare
☆19Updated 4 months ago
Alternatives and similar repositories for RecSys_Course_deepshare
Users that are interested in RecSys_Course_deepshare are comparing it to the libraries listed below
Sorting:
- 搜广推学习笔记:王树森“推荐系统”课程☆117Updated 9 months ago
- 推荐系统八股160问☆116Updated 5 months ago
- kaggle比赛—otto多目标推荐系统源代码,单模型分数0.594,LB排名30左右☆63Updated 2 years ago
- 机器学习,深度学习八股☆93Updated 5 months ago
- 零基础入门推荐系统 - 新闻推荐 Top2☆272Updated 4 years ago
- 天池大赛——新闻推荐场景下的用户行为预测挑战赛,SOLO赛,B榜排名5/5338☆51Updated 4 years ago
- ☆73Updated last year
- 本项目分享各种类型的推荐算法及实战代码,小白也可轻松掌握☆27Updated 2 years ago
- 代码随想录刷题思路总结(含代码)☆19Updated 9 months ago
- Awesome Generative Recommendation papers primarily focused on industry-level applications.☆117Updated 2 weeks ago
- 零基础入门推荐系统 - 新闻推荐 Top2☆26Updated 5 months ago
- 利用大模型赋能智能推荐,通过案例讲解大模型推荐算法原理,并且给出代码实现。☆90Updated 6 months ago
- https://tianchi.aliyun.com/competition/entrance/531842/introduction☆38Updated 3 years ago
- 基于天池新闻推荐赛数据集实现的新闻推荐☆17Updated 8 months ago
- ☆29Updated 3 months ago
- 🔥🔥🔥从零使用pytorch构建电影推荐系统,基于MovieLens数据集,实现了多种召回排序相关模型:DSSM、CF、MF、DeepWalk、Item2Vec,LR、Wide&Deep、DeepFM、DCNv1、DCNv2、DIN、MMoE、PLE。欢迎大家进行st…☆22Updated 3 months ago
- 搜索、推荐、广告、用增等工业界实践文章收集☆13Updated 2 years ago
- deepmatch模型使用天池新闻推荐大赛数据☆12Updated 10 months ago
- 算法岗笔试面试大全,励志做算法届的《五年高考,三年模拟》!☆586Updated 5 months ago
- 深度学习与推荐系统学习,理论结合代码更香。☆138Updated 3 years ago
- ☆44Updated last year
- 天池学习赛 零基础入门推荐系统 正式赛 第三名(0.2592) 开源代码☆55Updated 4 years ago
- Paper阅读记录博客(基于GitHub Action和GitHub Issue实现)。☆51Updated last year
- AI算法岗求职攻略(涵盖校招时间表、准备攻略(社招和校招)、刷题指南、内推和 AI 公司清单、求职算法必备资料等),算法方向涉及:机器学习、深度学习、计算机视觉、自然语言处理和搜广推等☆19Updated last year
- 王树森推荐系统公开课课程笔记☆16Updated 3 months ago
- 集成学习思维导图☆19Updated 2 years ago
- ☆181Updated 3 months ago
- kaggle Otto Recommender system code, single model LB0.596, about rank 22☆29Updated 2 years ago
- Continuously Updated Awesome Multimodal Recommendation Paper List☆38Updated 2 weeks ago
- A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.☆560Updated 2 weeks ago