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Description: 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 in the instrument of the algorithm. Directly on the Clustering the data movelens
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Size: 19456 |
Author: 张安站 |
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Description: The Duine Framework is a (collection of) software libraries that allows developers to create prediction engines for their own applications. A prediction engine is a component that predicts how interested individual users are in pieces of information. Such predictions can be used to personalise information to users, specifically in recommending to users what information is and is not of interest to them.
Duine is the Irish Gaelic word for person and is pronounced as “dinne” (diner without an ‘r’).
Duine has been developed by the Telematica Instituut/Novay and is based on scientific research on personalisation and specifically into recommender systems. The scientific research results on which the Duine software is based are available at the Telematica Instituut/Novay, you can download the pdf here
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Size: 11327488 |
Author: yan |
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Description: 推荐系统数据集MovieLens 1M数据集-Movilens dataset 1M
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Size: 6008832 |
Author: mars |
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Description: 【转】协同过滤代码，用于推荐系统，包括基于项目和基于用户两种情况。实现基于用户和基于项目的协同过滤算法。 实验所用数据为MovieLens – a web-based movies recommender system with 43,000 users & over 3500 movies. 保存在ga.mat文件用，由于ga.test测试集过于庞大，全部用来计算的话耗时庞大，所以每次计算时随机选择部分，具体函数的使用请参照probar.m。我所得到的实验结果保存在results1-results8.mat里面。-[Turn] collaborative filtering code for the recommended system, including project-based and user-based two cases. User-based and project-based collaborative filtering algorithm. Experimental data for MovieLens- a web-based movies recommender system with 43,000 users & over 3500 movies. Saved in ga.mat file with Because ga.test test set is too large, all used to calculate the time-consuming massive, so every time The calculated random selection portion, the use of the specific function, please refer to probar.m. I obtained experimental results are saved inside results1-results8.mat.
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Size: 2553856 |
Author: Singel |
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Description: 基于内容的推荐算法的实现，使用movielens数据集，JAVA语言-Content-based recommendation algorithm implementation, use movielens data set, the Java language
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Size: 25600 |
Author: 兰陵悦之 |
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Description: movielens 的数据集 100k 协同过滤算法实验必备-movielens datasets
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Size: 4945920 |
Author: jacksun |
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Description: 改程序为Java实现的一个HMM模型用于经典数据集MovieLens的实践，用于预测人们的观影倾向。-Reform program is a Java implementation of HMM model for classic dataset MovieLens practice, used to predict people s viewing tendencies.
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Size: 5120 |
Author: 呼啸卫 |
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Description: 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
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Size: 2197504 |
Author: xian |
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Description: 推荐系统实战代码 movielens数据集-Recommended system code movielens actual data sets
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Size: 564224 |
Author: 刘雅丽 |
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Description: The code is for parametric probabistic matrix factorization (PPMF) in the paper "H. Shan and A. Banerjee. Generalized Probabilistic Matrix Factorization for Collaborative Filtering. ICDM, 2010".
runppmf.m: An example on how to run the code.
ppmfLearn.m: learning process of ppmf. It calls ppmfEstep.m and ppmfMstep.m.
ppmfEstep.m: Variational E-step.
ppmfMstep.m: Variational M-step.
ppmfPred.m: predict on the test set.
data.mat: Sample data (movielens data).
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Size: 312320 |
Author: simon |
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Description: 利用python实现基于用户的协同过滤推荐，采用pearson相关系数计算相似度，加权平均预测分数-Users use python-based collaborative filtering recommendation, pearson correlation coefficient is calculated using the similarity, the weighted average prediction scores
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Size: 4747264 |
Author: don |
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Description: 压缩文件中包含一下列表： 1，movielens 公开实验数据集(推荐系统研究经常用到~) 2，模拟预测评分的python代码（python3.x） 希望对大家学习有所帮助。有问题可以邮箱联系。-movielens data mining knn
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Size: 4745216 |
Author: jinbiao |
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Description: 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
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Size: 2048 |
Author: 开发费 |
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Description: 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
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Size: 2048 |
Author: 章旭 |
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Description: 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 initiative for the establishment of user recommended product recommendation system. Reference implementation or its collaborative filtering algorithm optimized to achieve and improve the algorithm to calculate each customer not purchased the degree of interest to the customer the initiative to recommend N of products he is most interested. Experimental data can be downloaded MovieLens.com. It requires the use of at least 10,000 different user data, at least 1,000 different movie.
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Size: 10259456 |
Author: way |
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Description: 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
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Size: 811008 |
Author: zhaohui |
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Description: 电影推荐系统 协同过滤推荐 Java源码-MovieLens Colleboration Filtering Recommendation System
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Size: 262144 |
Author: 王 |
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Description: movielens 数据集 可供推荐系统算法跑代码使用-movielens dataset recommendation system algorithm for use to run the code
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Size: 2192384 |
Author: 王 |
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Description: 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)
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Size: 6019072 |
Author: dohuasinan
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Description: “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] (http://files.grouplens.org/datasets/movielens/ml-1m.zip) and unzip it to the project MovieLens-RecSys folder")
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Size: 5120 |
Author: frank008 |
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