协同过滤算法

一种基于Rough集理论的最近邻协同过滤算法

A Nearest-Neighbor Collaborative Filtering Algorithm Based on Rough Set Theory

SlopeOne算法是基于项目协同过滤算法的简化和改进算法，该算法的简洁特性使它的实现简单而高效，而且具有较好的精确度。

Slope One algorithm is an item-based collaborative filtering algorithm , the algorithm is simple and features make it efficient and better accuracy .

针对这些问题，本文提出了一种将基于用户协同过滤算法和SlopeOne推荐算法相结合的新算法，提高算法的准确性和效率。

Based on this , this paper presents a new algorithm that combining the user-based collaborative filtering algorithms and Slope One recommendation algorithm to improve the accuracy and efficiency .

传统的协同过滤算法把顾客描绘成商品的N维向量，其中N是登记在册的不同商品的数量。

A traditional collaborative filtering algorithm represents a customer as an N-dimensional vector of items , where N is the number of distinct catalog items .

然后选取协同过滤算法作对照，并采用MovieLens站点提供的测试数据集。

Then collaborative filtering algorithm is chosen as a contrast and the test data set provided by Movie Lens web site is adopted .

基于SVD的协同过滤算法的欺诈攻击行为分析

Analysis of shilling attacks on SVD-based collaborative filtering algorithm

基于属性相似性的Item-based协同过滤算法

Item-based collaborative filtering algorithm using attribute similarity

提出了一种基于Web服务的智能网页推荐系统，该系统使用了基于Item-to-Item的协同过滤算法，并通过对一个实例的研究验证了所提出的算法以及推荐系统的有效性。

A new intelligent recommendation system is presented based on Web service by Item-to-Item collaborative filtering algorithm . And the effectivity of the algorithm and the recommendation system is validated by an instance research as the relevant experiment result .

针对基于项目的协同过滤算法不能实现“跨类型”推荐的缺点，本文提出了一种新的基于关联性评分预测的协同过滤算法IAPCF。

An item-association-prediction-based collaborative filtering algorithm ( IAPCF ) is proposed to overcome the shortcomings of the traditional item-based collaborative filtering algorithms .

协同过滤算法中推荐集选取方法的研究

Study on the selection method of recommendation in Collaborative Filtering Algorithm

一种基于用户特征和时间的协同过滤算法

A Collaborative Filtering Recommendation Based on User Characteristics and Time Weight

相当一部分现有的协同过滤算法使用权重和的办法形成预测。

Most of existing collaborative filtering make predictions using weighted average method .

基于矩阵划分和兴趣方差的协同过滤算法

Collaborative Filtering Algorithm Based on Matrix Partition and Interest Variance

基于商品属性隐性评分的协同过滤算法研究

Research on collaborative filtering algorithm based on item 's attribute implicit rating

协同过滤算法是目前应用广泛的一种个性化推荐技术，传统的协同过滤算法又分为基于用户和基于项目的协同过滤。

Collaborative filtering is a widely used technology of personalized recommendation systems .

目前，协同过滤算法是推荐系统中最流行的一个算法类别。

Collaborative filtering is the most popular category of algorithms in recommendation systems .

基于回归分析的信息协同过滤算法预测研究

A Research on Regression Analysis-Based Collaborative Filtering Algorithm Prediction

基于页面兴趣度的协同过滤算法研究

Collaborative Filtering Algorithm Research Based on Page Interest Degree

一种压缩稀疏用户评分矩阵的协同过滤算法

Collaborative filtering algorithm via compressing the sparse user-rating-data matrix

一种改进的基于流形对齐的协同过滤算法

An Improved Collaborative Filtering Algorithm Based on Manifold Alignments

本文提出了一种结合人口分类特征计算用户相似度的协同过滤算法。

A collaborative filter algorithm by taking demographic trait into consideration is proposed .

协同过滤算法是最有效的推荐系统技术之一。

Collaborative filtering is one of the most promising techniques for recommender systems .

线性逐步遗忘协同过滤算法的研究

Research on Lineal Gradual Forgetting Collaborative Filtering Algorithm

协同过滤算法中一种改进的相似性计算方法改进的多种群协同进化微粒群优化算法

An Improved Method of Calculating Similarity in Collaborative Filtering Multi-species cooperative particle swarm optimization algorithm

之后，本文将提出的浏览购买关系模型分别用于改进基于用户和基于项的协同过滤算法。

Then we implemented the proposed purchase prediction model to improve the collaborative filtering algorithm .

本文主要是研究和解决协同过滤算法中的冷启动推荐问题。

This article is mainly to study and tackle cold-start problems in collaborative filtering methods .

融合多系统用户信息的协同过滤算法

Collaborative Filtering Algorithm Fusing Multi-system User Information

但传统的协同过滤算法存在着稀疏性、扩展性和同义性的问题。

But traditional collaborative filtering algorithm has the shortcomings of sparseness , expansibility , and synonymy .

在众多个性化推荐技术中，协同过滤算法是当下研究的热门。

Among so many personalized recommendation technologies , collaborative filtering algorithm is the hot research currently .

对于协同过滤算法，通过实验讨论了它的一些关键参数的选择依据，最后通过实验验证算法的可行性。

We discuss how to get some important parameters ' values of collaborative filtering algorithm through experiment .