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UserCF和ItemCF协同过滤推荐算法的实现
本项目使用两种算法来实现一个电影推荐系统,一个是CNN,另一个是矩阵分解的协同过滤。
利用用户画像数据、物品元数据、用户行为数据为特征构建推荐系统,使用CB和CF算法做推荐召回,使用 Redis数 据库做缓存处理,结合机器学习LR算法推荐排序,达到粗排、精排效果,实现推荐引擎搭建。
整理机器学习,深度学习,自然语言处理,推荐系统相关的各种学习笔记
个性化新闻推荐系统,A news recommendation system involving collaborative filtering,content-based recommendation and hot news recommendation, can be adapted easily to be put into use in other circumstances.
对收集的法律文档进行一系列分析,包括根据规范自动切分、案件相似度计算、案件聚类、法律条文推荐等(试验目前基于婚姻类案件,可扩展至其它领域)。
:sunrise: 基于用户的协同过滤算法实现的商品推荐系统 https://github.com/MrQuJL/product-recommendation-system
Mining , pre-processing and embedding over 1 million Amazon Movie & T.V. reviews to build a multi class Naive Bayes model and later a CNN-LSTM model (that uses the Naive Bayes model as a baseline) to predict rating from text. Interpreting the original classifier using local surrogate models using the LIME library. Using LDA topic modeling to build a theme based recommender from the reviews and using a model based collaborative filtering system using SVD matrix factorization to build a second recommender system.
实现了基于协同过滤(UserCF)的模型、基于隐语义(LFM)的模型、基于图(PersonalRank)的模型,并结合三种模型的结果给出最终结果的推荐算法
A Recommendation System based on Tensorflow using FNN and CNN.
记录学习推荐系统的demo
分别基于协同过滤算法和基于TensorFlow建立推荐系统
一些传统推荐算法的实现,包括基于内容的推荐,协同过滤,矩阵分解
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches"
分布式租房爬虫系统
基于用户协同过滤的推荐算法
tensorflow实战练习,包括强化学习、推荐系统、nlp等
基于thinkphp5.0开发的房屋中介系统
基于协同过滤算法的学习资源个性化推荐系统(硕士毕设)
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