个性化推荐

这就是所谓的Web个性化推荐技术，是Web技术研究和应用的热点之一。

This is defined as the technique of web Personalized recommendation .

一种基于后项不定长关联规则的Web个性化推荐方法

A Web Personalized Recommendation Method Based on Uncertain Consequent Association Rules

基于Web日志和缓存数据挖掘的个性化推荐系统

Personalization Recommendation System Based on Web Log & Cache Data Mining

基于内容的Web个性化推荐技术研究

Research on Technologies of Web Personalized Recommendation Based on Content

设计并实现基于Web日志挖掘的个性化推荐系统原型。

Design and implement a web log based personalized prototype recommendation system .

基于WEB使用挖掘的个性化推荐系统的研究

Research of Personalized Recommendation System Based on Web Usage Learning

Web挖掘是实现Web个性化推荐的关键技术之一。

Web mining is one of the key technologies of web personal recommendation .

Web数据挖掘在个性化推荐服务的应用

Web data mining used in the personalized recommendation service

基于投票机制的Web个性化推荐系统

The Web Personalized Recommender System Based on Voting Mechanism

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agent技术和反馈机制的个性化推荐方法研究与设计

A new approach of personalize recommendation uses agent technology and feedback mechanism

基于Agent的个性化推荐系统的研究

Research on Agent-Based Personalized Recommendation System

基于混合隐Markov链浏览模型的WEB用户聚类与个性化推荐

Web User Clustering and Personalized Recommendation Based on Mixtures of Hidden Markov Chain Models

个性化推荐是一种已经被公认的能够有效解决internet信息过载的方法。

Personalized Recommendation Services has been considered as a typical method to deal with the problem of internet mass information .

Web个性化推荐系统根据用户的浏览模式预测用户需求，并向他们提供个性化的推荐服务。

Web personalized recommender systems anticipate the needs of web users and provide them with recommendations according to their navigation patterns .

因而基于投票机制的Web个性化推荐系统以及技术的实现成为研究者热点关注的方向。

So , the implementation of web PRSs based on voting mechanism and technologies , is becoming a hot topic for researchers .

随着Internet和网上购物的迅猛发展，网上购物系统中的个性化推荐技术逐渐成为人们研究的一个重要领域。

With the rapid development of Internet and the online shopping system , the technology of personalized recommendation has become an important field gradually .

如果说过去的Internet是搜索技术大行其道的时代，那么未来的Internet将属于个性化推荐技术。

If the past of the Internet is a popular search technology era , the future of the Internet will belong to personalized recommendation technology .

阐述了Web日志挖掘的概念和步骤，描述了个性化推荐的概念、分类、核心技术和步骤。

It also introduces Web Log Mining on the concept and steps , describes personalized recommendation on the concept , classification , core technologies and procedures .

个性化推荐系统（RecommenderSystem）作为一种信息过滤的重要手段，是当前解决信息超载问题的非常有潜力的方法。

The personalized recommender system as a important information filtration mean is a potential method to solve the problem of information overload currently .

基于E-Learning的社区监控及个性化推荐系统的实现

Realization and Research on Community Monitoring & Personalized Recommendation System Based on E-Learning

设计了一种基于Agent元搜索引擎的个性化推荐系统的框架，包含人机交互、用户兴趣学习、系统数据管理、信息搜索、多Agent协同等功能。

This framework mainly contained the functions of human-computer interaction , user interest learning , system data management , information search , agent collaboration and so on .

系统在结构上分为离线模块和在线模块，离线模块包括原始数据的预处理和挖掘算法的运行：在线模块使用个性化推荐引擎向客户提供个性化Web页面推荐。

The system architecture separates the offline process of data preparation and Web mining , and the online process of customizing Web pages based on a user 's active session .

利用Web日志文件采用网页被用户选择的频率作为权重值，实现了个性化推荐系统的算法。

By using Web log file , making use of the Web page frequency which is visited by users as its weight , the algorithm is implemented in the personalization recommendation .

CtoC电子商务站点中的Web个性化推荐技术

Web Personalization Recommendation Technologies in CtoC E-commerce Websites

基于TOPSIS算法的个性化推荐研究

Research on the Personalized Recommendation Based on TOPSIS 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 .

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 .

通过将游戏玩家需求特征、网络游戏特征和网络游戏虚拟物品特征分类、量化分析，建立了基于QFD及改进BP算法的网络游戏个性化推荐系统。

Trial version of recommendation system in lab is built based on the classification of game , gamer and virtual items by the using QFD and BP neural network .

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 .