搜索资源列表
k-means
- 实现了K均值算法,可以对movielens上的数据进行自动分类,给出推荐值,是数据挖掘中的信息推介必要的算法工具。可以直接对movelens的数据进行聚类-Implementation of the K-means algorithm, can movielens on automatic classification of data, recommend give the value of data mining are to promote the necessary information
KNN-TOPN
- 以movielens为数据集写的TOP—N推荐系统,基于KNN算法-Write to movielens dataset TOP- N recommendation system, based on KNN algorithm
test.tar
- 用于推荐系统的svd算法,以movielens 1m数据集为例,并有调参数方法-an implemention of svd algorithm for recommender
rec
- Java实现将movielens各种规模数据的划分为测试集和训练集-Split movielens dataset to trainset and test set
recommender-
- Collaborative Filtering,基于Collaborative Filtering,建立主动为用户推荐商品的推荐系统。实现参考协同过滤算法或它的优化,实现并改进算法,计算出每个客户对未购买的商品的兴趣度,并向客户主动推荐他最感兴趣的N个商品。实验数据可以从MovieLens.com下载。要求使用至少10,000不同用户的数据,至少1000个不同的movie。-Collaborative Filtering,Based Collaborative Filtering, the in
TimeInterval
- 推荐系统的攻击检测,利用偏态分布的思想进行判断,数据集为movielens-Detect Shilling attack of Collaborative Filtering
fenlei
- 针对广泛用于推荐算法研究的movielens数据集,本程序用于统计用户评分时间,寻找用户评分规律。-Recommendation algorithm is widely used for research movielens data set, the program for statistical Rating time, looking user rating rule.
Recommender
- 基于MovieLens数据,通过计算余弦相似度,Python语言构建的一个简单协同过滤推荐系统,并给出RMSE等测评结果-Based MovieLens data by calculating the cosine similarity, Python language to build a simple collaborative filtering systems, and the like are given RMSE uation results
CF
- Python实现协同过滤算法,即Collaborative Filtering(CF),数据集为MovieLens电影推荐和书籍推荐数据集-Python implementation of collaborative filtering algorithm, namely Collaborative Filtering (CF), the data set is recommended MovieLens movie and book recommendations datasets
SVD++
- 简单的SVD基于movielens的开发python程序(this is a simple SVD write by python base on movielens dataset)
MovieLens-RecSys-python2
- 基于Movielens 1M数据集分别实现了User Based Collaborative Filtering(以下简称UserCF)和Item Based Collaborative Filtering(以下简称ItemCF)两个算法.(Implementation of collaborative filtering based on UCF/ICF)
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]
movie-recommendation-python-master
- 利用协同过滤算法对movielens中的数据进行电影推荐(Collaborative filtering algorithm for movie recommendation in movielens)