搜索资源列表
CF-toolkit-(1)
- 基于Matlab的推荐系统源码,来源于协同过滤算法-matlab CF
MATLAB
- MATLAB系统辨识与仿真,不错的书~推荐一下-MATLAB system identification and simulation, a good recommendation about the book ~
A
- movie lense的用户推荐数据,用于用户推荐系统的仿真与研究-movie lense user recommendation data, the user recommendation system for simulation and study
bregcc_code
- Bregman 双聚类matlab实现,推荐系统算法-Bregman co-clustering matlab encode,recommendation system
UnresystCD
- 传上去的为数据挖掘推荐系统源码,大家可以下载使用-Recommendation algorithm source code
colaberate-filtering
- 协同过滤 推荐系统 基于共同评分的用户相似度计算-user simularity compute
MyMediaLiteJava
- 推荐系统的源代码,具有当今最新的推荐算法实现,有众多应用。比如作为插件应用于开源数据挖掘工具RapidMiner中。-Source code for recommendation systems, with state-of-the-art algorithms for recommendation and widely applicable. It can be used as plugin in the open source data mining tool RapidMiner.
svdfeature-1.1.6
- CF svdFeature, 基于C++开发的,利用svd奇异矩阵分解建立的协同过滤工具箱。可以解决常用的所有协同过滤问题。对于推荐系统的建立至关重要,是很好的学习和使用的工具箱。协同滤波也是最有机器学习感觉的方法之一,我们大家都爱它!-CF svdFeature, a well performed toolkit of confiltering method based on svd, which is developed using C++ programming language. It
ASBDMiningTool
- 一个小型的文档推荐系统,改推荐系统主要基于内容的方法进行推荐。-An smart file recommend system which is based on content Similarity。
BaiduRe
- 推荐系统中的二部图,热传导推荐具体实现过程-Recommended system two maps, heat conduction recommend specific implementation process
project
- 数据挖掘,推荐系统,堆叠降噪自编码器,逻辑回归(Data mining, recommender systems, stack noise reduction, self coder, logic regression)
recommend
- 一个关于自己实现推荐系统的案例,搞清楚推荐系统的实现过程(A recommendation on the implementation of their own system, clear the recommendation system to achieve the process)
g13tsr
- 机器学习启蒙实战学习源码,回归模型,分类模型,聚类和相似度模型,推荐系统,深度学习等学习代码。(Machine learning, practical combat learning source,regression model, classification model, clustering and similarity model, recommendation system, depth learning and other learning code.)
cross-filter
- 电影推荐系统,运用协同过滤算法,运行ex8_cofi进入主程序(The application of collaborative filtering algorithm in movie recommendation system)
recommender
- 利用scala实现的推荐系统,其中用到了hive、kafka、scala等(A recommendation system implemented using Scala, which uses hive, Kafka, Scala and so on)
MovieLens-RecSys-master
- “推荐系统实践”,项亮,代码。数据“下载Movielens 1M数据集[ml-1m.zip](http://files.grouplens.org/datasets/movielens/ml-1m.zip),并解压到项目MovieLens-RecSys文件夹下”("Recommending system practice", light, code. The data "downloads the Movielens 1M data set [ml-1m.zip]
SVDRecommenderSystem-master
- 实现了推荐算法,协同过滤、基于内容的推荐算法、奇异值分解。(Implementation of recommendation algorithm, collaborative filtering, singular value decomposition and so on.)
recommend-system-master
- 协同过滤算法实现推荐过程,其中产生了协同过滤推荐矩阵,通过矩阵计算推荐数据(generate recommend result through ITEMCF)
CollaborativeFiltering-master
- 协同过滤算法的实现,基于协同过滤算法的推荐系统,在电子商务领域有着极为广泛的应用。(Collaborative Filtering)
电影推荐算法
- 电影推荐系统代码,使用了迭代SVD算法,其实是老师布置的期末作业,正确性已由老师验证(Movie recommendation system code)