个性化推荐系统

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 .

基于矩阵聚类的电子商务网站个性化推荐系统

Personalized Recommender Systems Based on " Matrix Clustering " for E commerce

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

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

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Agent的个性化推荐系统的研究

Research on Agent-Based Personalized Recommendation System

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 .

基于P2P网络的自组织个性化推荐系统（英文）

A Self-Organized Personalized Recommendation System Based on Peer-to-Peer Networks ;

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 .

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 .

基于以上背景，本文设计并实现了一个改进的个性化推荐系统，该系统将Web内容挖掘及结构挖掘的技术应用到Web使用挖掘的过程中，用以提高推荐的质量。

This paper designs and implements a novel web recommender system , which combines usage data , content data and structure data in a web site to improve the quality of web site recommendation .

基于Web数据挖掘的个性化推荐系统，主要分为离线和在线两部分，离线部分对数据预处理模块和模式挖掘模块进行模式分析，在线部分主要利用离线部分提供的模式对在线用户进行推荐。

Offline part of the data preprocessing module and the model for web data mining module pattern analysis , online using offline section provides some of the major mode of use of online users recommended .

本文系统地阐述了Web数据挖掘相关理论知识及其在真实Web环境中的整个挖掘流程并对其在个性化推荐系统中涉及的各种挖掘算法进行了深入研究。

This paper systematically expounded Web data mining algorithms and its entire mining process in real Web environment , then this paper researched and analyzed related Web data mining mining algorithms involved in the personalized recommendation system .

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 .

实验证明在个性化推荐系统中PARM算法的效率明显高于FP-Growth算法。

Experiments show that PARM algorithm is more effective in personalized recommendation than FP-Growth algorithm .

近些年来，随着个性化推荐系统在Amazon等公司的成功应用，以及互联网技术的不断更新，推荐系统的研究也取得了一定的成果。

In the past years , recommendation system studies have yielded some results , with successful applications of personalized recommendation system in companies such as Amazon , as well as Internet technologies updating .

在建设数字化终身学习体系的大背景下，E-Learning个性化推荐系统作为终身学习体系中最重要的学习方式，受到广泛的关注。

In the background of digital lifelong learning system construction , E-learning personalized recommendation system , which is one of the most important systems of lifelong Learning , has been widespread concerned .

近年来，随着Internet和信息技术的飞速发展，日益严重的信息过载和信息迷向问题助推了个性化推荐系统的蓬勃发展。

In recent years , with the rapid development of the Internet and information technology the problem of information overloading and information amazing , which we are having been frustrated , has became more and more worse . And all these boost the flourishing development of personalized recommendation systems .

获取Web日志数据、页面内容及站点结构信息，将它们作为个性化推荐系统的数据源，并针对Web中文网页以及个性化推荐系统的特点，对数据进行预处理，以提高用户访问模式识别的精确度。

Collect web data from server log , page content and site topology as data source of the web personalized recommender system , furthermore , make preprocess according to the characteristic of Chinese web pages and recommender system , for the purpose of acquiring users ' navigational patterns more exactly .

个性化推荐系统（简称PRS）最早应用于电子商务和信息服务领域，现已相对成熟。

Personalized recommendation system ( hereinafter referred to as PRS ) applied to the fields of e-commerce and information services early , and has been relative mature .

本文全面介绍了推荐系统的概念、分类，以及基于投票机制的个性化推荐系统的概念、常见的推荐技术，着重分析了Web个性化推荐系统的主要实现技术以及基于BIRT技术的图表分析技术。

The paper introduces the concepts and classification of recommender systems , the concepts of web PRSs based on voting mechanism and common recommender technologies . It gives an emphasis on main realization technologies of web PRSs and chart analysis based on BIRT technology .

本文通过对当前B2C网站的电子商务个性化推荐系统分析，发现传统的推荐系统有如下问题：数据稀疏性问题，用户购买或评分的只占总商品数的1%左右；

There are several problems in traditional systems from the current B2C website electronic commerce personalization recommendation system : data sparsity , the commodities which are purchased or rated by users only occupy the total commodity number about 1 % ;

个性化推荐系统中遗漏值处理方法的研究

Study on Processing Method of Missing Values in Personalized Recommendation Systems

基于客户浏览行为的个性化推荐系统研究

The Study of Personalized Recommendation System Based on Client Browser Behaviors

在此背景下，个性化推荐系统应用而生。

Under this background , the personalized recommendation system is emerging .

目前它已被成功地应用于个性化推荐系统中。

Now it has been used in personalized recommendation system .

数字化咨询系统包括虚拟咨询系统和个性化推荐系统，前者侧重提供指南型和指导型咨询；

Digitalized consulting system includes virtual consulting system and personalized recommendation system .