推荐算法

基于网页关键词的个性化Web推荐算法

A Personal Web Recommendation Algorithm Based on Web Page Key Words

基于web数据挖掘的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on Web Data Mining

基于主成分分析法的Web页面推荐算法

The Web Page Recommend Algorithm based on Principal Component Analysis

基于Web挖掘的一种个性化推荐算法

A personal recommendation algorithm based on Web mining

基于多Agent的智能推荐算法设计

Algorithm Design for Multi-agent Based Intelligent Recommendation System

通过三类Agent之间的协作，来实现此用户兴趣模型和图书推荐算法。

User interest model and book recommending algorithm are implemented by collaborations of these three types of Agent .

实验结果表明，本算法较之传统推荐算法和SlopeOne算法在平均绝对误差值上有一定的提高，证明了本算法的可行性与有效性。

Experimental results show that the algorithm is better in the mean absolute error against traditional algorithm and the Slope One algorithm .

使用BP神经网络缓解协同过滤推荐算法的稀疏性问题

Employing BP Neural Networks to Alleviate the Sparsity Issue in Collaborative Filtering Recommendation Algorithms

基于BP神经网络的协作过滤推荐算法

BP Neural Networks-Based Collaborative Filtering Recommendation Algorithm

建立一个模型，对多网站的分布式、松散、异构的信息进行整合，提出一种复杂分布式Web聚合推荐算法。

Build a model of distributed multi-site , loose , heterogeneous information integration , Web aggregation presents a complex distributed recommendation algorithm .

基于P2P网络的协同过滤推荐算法的研究与实现

Research and Implementation of Collaborative Filtering Recommendation Algorithm Based on P2P Network

最后对本文提出的推荐算法进行了实现，并设计实现了上下文感知的Web服务推荐系统。

Finally , the service recommendation algorithms presented in this paper are implemented and context-aware web services recommendation system is also designed and implemented .

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 .

在Web挖掘的基础上设计针对Web服务的Web访问事务模型WTM和个性化推荐算法。

Based on Web data mining , a Web access transaction model WTM and personal recommendation algorithm were presented .

这些推荐算法主要应用于B2C的电子商务系统中。

These recommendation algorithm is mainly used in B2C e-commerce system .

本文的创新之处：尝试设计了一个基于Web日志挖掘的智能Web站点系统模型和其中的预测推荐算法。

The innovation of this dissertation is : It attempts to design a intelligent Web site system model based on Web Usage Mining and the algorithm of pre-judging and recommending .

这种上下文感知的Web服务推荐算法选择和执行策略不仅实现了服务推荐算法选择的个性化，而且实现了服务推荐结果的个性化。

This context-aware Web service recommendation algorithm selection and execution strategy not only realizes the personalization of service recommendation algorithm selection , but also realizes the personalization of service recommendation results .

改进方案在一定程度上解决了CF推荐算法遇到的矩阵稀疏性问题、冷启动问题和可扩展性差问题。

This improved program has solved the scalability , sparsity , cold start problems in a certain extent .

本文调研了多种推荐算法，研究了在LBS中根据用户历史位置进行信息推荐方法。

The author compared several recommendation algorithms and brings out a user-history-position based recommendation method .

同时，本文中采用的基于Web日志挖掘的个性化推荐算法，经测试结果证明，具有较高的查准率，有一定的实用价值。

At the same time , the personalized recommendation algorithm that the paper used based on Web log mining be tested and the results prove that with high precision , and a certain degree of practical value .

使用一种树形结构来存储挖掘得到的Web访问序列模式，在该树形结构的基础上进行页面匹配，给出了一种基于访问序列模式的页面推荐算法。

Web access sequential patterns are stored via a type of tree structure and the page matching is based on the tree structure . Then , the page recommendation algorithm based on access sequential pattern is proposed .

介绍了Web挖掘的基本情况，分析了Web使用信息挖掘的步骤.提出了基于Web使用信息挖掘的个性化推荐算法，并将其运用到网络教学中，得出了个性化的网络教学体系结构。

And the process of Web usage mining was simply analyzed . Based on Web mining , personal recommendation algorithm was presented . Finally , a system structure of Web education based on the algorithm was put forward .

然后，我们提出了将领域本体论与web使用记录挖掘和个性化过程相结合的一个总体框架。给出了推荐算法以及相关性推荐算法，并且与以前的推荐算法进行了实验比较。

Finally , we present a general framework for fully integrating domain Ontology with Web Usage Mining and Personalization processes , give algorithm of recommendation and relativity algorithm of recommendation , and compare with formerly algorithm of recommendation by lab.

当然任何事物都有两面性，CF推荐算法也是如此，它面临着一些问题，比如系统可扩展性差、冷启动、矩阵数据稀疏等等。

On the other side , CF is confronted with some problems , such as scalability , sparsity , cold start and so on .

运用这个真实数据集对基于标签的推荐算法、TF算法以及基于人工鱼群模型的推荐算法进行验证。

Based on these real datasets , we do extensive experiment to test the tag-based recommendation algorithm , TF algorithm and AFSA .

实验结果表明，TF算法和基于人工鱼群模型的推荐算法比基于标签的推荐算法在查准率、查全率方面性能得到明显提升。

Result shows that TF algorithm and AFSA have significant performance improvement than tag-based recommended algorithm in precision radio and recall radio .

在基于认知Agent的个性化推荐算法上，本文提出认知Agent与虚拟环境的结合方法，解决了传统认知Agent处理行为单一和情感表达问题。

On the personalized recommendation algorithm based on multi-agent , the thesis proposes the method to integrate the cognitive agent with virtual environment , to solve the problems of the single processing behavior and sensibility expression for the traditional cognitive agent .

如果是一个老用户，将调用协同过滤推荐算法产生推荐集，使用户在登录的初始阶段就能获得个性化的RSS广告。

It will call collaborative filtering algorithms to get sets of ads recommendation for registered users . Then registered users can get the personalized RSS ads when they login .

为了避免P2P网络中存在的消息的泛洪问题、以及提高推荐算法的效率，采用一种基于兴趣域的增量计算相似度的方法。

In order to avoid flooding that exist in P2P networks , and improve the efficiency of the algorithm , we use a method of incremental calculation of similarity based on interest-domain to calculate the similarity .

将基于客户兴趣模型的节目个性化推荐算法应用于电信iTV预处理系统中，实现了理论与应用的结合。

The programs personalized recommendation algorithm based on customer interest model is applied to telecommunications ITV pretreatment system , to achieve a combination of theory and application .