Collaborative Filtering

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.

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。

Model of Collaborative Filtering in E-commerce Recommender System Based on Interest Measure

基于兴趣度的协同过滤商品推荐系统模型

Time weighted collaborative filtering algorithms based on resources category

基于资源类的时间加权协作过滤算法

Research on Information Product Personalized Bundling Recommendation Base on Collaborative Filtering

基于协同过滤的信息商品个性化捆绑推荐研究

Research on Campus Educational Resources' Personalized Recommendation Based on Collaborative Filtering

基于协同过滤的校园教育资源网个性化推荐研究

In order to serve users with more accurate information, the Agent collaborative filtering model& ACFM based on users 'feedback and the dynamic Q learning algorithm are put forward, and users' interesting model is built.

为了提供给用户更准确的信息，提出基于用户反馈的智能合作过滤模型和一种基于用户兴趣的动态Q学习算法，并建立用户兴趣模型。

Experimental results show that this method is of low memory requirements and lower mean absolute error ( MAE) value, and provides better recommendation quality compared with traditional collaborative filtering algorithms.

实验表明，这种方法的优点是低内存需求，具有较小的平均绝对偏差值，并且显示出了比传统推荐算法更好的推荐质量。

TTL has some novel features over traditional collaborative filtering on cross domain.

TTL和传统的基于迁移学习的协同过滤推荐系统相比有一些新的特征。

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

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。

With the more and more complex site structure of recommended system, the increasing number of items and users, collaborative filtering algorithm exposed many problems in the implementation process.

随着推荐系统的站点结构越来越复杂、内容和用户数量越来越多，协同过滤算法在实现过程中暴露出很多问题，本文研究数据稀疏处理、预测准确性和可扩展性三个问题。

Collaborative filtering is the most widely used and most successful recommendation technology.

协同过滤是推荐系统中采用最为广泛也是最为成功的推荐技术。

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

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。

The major work done is reflected in the following areas: This thesis has analyzed the shortcomings of collaborative filtering algorithms, and proposed the combined recommendation algorithm of collaborative filtering algorithms which is based on item attributes.

本文所做的主要的工作体现在下面的几个方面：分析了协同过滤算法缺点，针对其缺点提出基于项目属性矩阵的协同过滤的组合推荐算法。

Collaborative filtering is one of the most widely used and successful methods for recommendation, which has been made fast development in theoretical research and applications.

协同过滤技术是推荐系统中最广泛使用和最成功的技术之一，在理论研究和实践中都取得了快速的发展。

Finally, the traditional user collaborative filtering recommendation system and the proposed collaborative personalized recommendation system were compared to proved by data verify that the improvements of precision of proposed system. the realization of prototype also can prove the feasibility of the proposed system.

最后，本文利用对传统的用户协同过滤推荐系统与本文提出的个性化协同推荐系统进行了对比，证明了系统在推荐精度方面的改进，并通过原型系统的实现证明了本文提出的系统的可行性。

The advantage of the framework structure is its multi-engines. Because of its independent of the engine structure, the system can be flexibly adjusted according to the algorithm. We introduce the framework of the recommendation engine for collaborative filtering algorithm combined with Non-negative matrix factorization.

该框架的优势在于它的多引擎结构，各引擎之间结构独立，可以根据算法灵活调整，本文详细介绍了结合非负矩阵分解的协同过滤推荐算法使用的引擎框架。

Then, it proposed a user-feature-based recommendation algorithm and an improved item-based collaborative filtering algorithm, which to some extent alleviate the major challenges the recommender system are facing.

在此基础上，提出了基于用户特征的推荐算法和改进的基于项目的协同过滤算法，从而在一定程度上缓解了推荐系统面临的主要挑战。

Method Research of Network Product Bundling Based on Association Rules and Collaborative Filtering

基于关联规则和协同过滤的网络商品捆绑方法研究

Traditional collaborative filtering algorithm is a weighted average prediction algorithm based on nearest neighbors'ratings.

传统协同过滤算法最后的预测值是用户最近邻评价的加权平均值,过于强调相似度的作用.

Collaborative Filtering Recommendation Algorithm Based on Comprehensive Interest Measure

基于综合兴趣度的协同过滤推荐算法

The paper put forward an improved collaborative filtering algorithm based on symbolic data analysis, which apply singular value decomposition and symbolic data analysis method to recommended system.

本文提出一种改进的基于符号数据的协同过滤推荐算法，即将奇异值分解和符号数据分析方法结合起来运用到推荐系统中。

A Survey of Collaborative Filtering Algorithm Applied in E-commerce Recommender System

电子商务推荐系统中的协同过滤推荐非线性滤波的迫近

At present mainly for collaborative filtering problems, conduct research in different ways.

当前主要是针对协作过滤存在的问题，进行不同的方法研究。