1、

Personalization Recommendation System Based on Web Log& Cache Data Mining

基于Web日志和缓存数据挖掘的个性化推荐系统

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2、

Research of Personalized Recommendation System Based on Web Usage Learning

基于WEB使用挖掘的个性化推荐系统的研究

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3、

For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system.

为此,本文运用柔性理论对电子商务推荐系统进行分析,提出柔性电子商务推荐系统。

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4、

Research on the Web Log Based Internet Collaborative Recommendation System

基于Web日志的因特网协作推荐系统的研究

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5、

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

设计并实现基于Web日志挖掘的个性化推荐系统原型。

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6、

At last, the paper analysis the applications of web logs mining system, designs and implements a news recommendation system using Java script.

最后分析了日志挖掘在新闻网站的应用,实现了新闻推荐系统。

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7、

The proposed content recommendation system makes recommendations based on which photo the user is currently viewing.

这样的推荐系统的特点是专门针对用户当前正在浏览的对象进行推荐。

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8、

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。

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9、

The pricing strategy for e-commerce auction Recommended system the bid recommendation system characteristics, detailed analysis of the the common enterprise businesses use auction Recommended pricing strategy means.

本文针对电子商务竞价推荐系统中的定价策略,分析了竞价推荐系统的特点,同时详细分析了企业商家在使用竞价推荐时常用的定价策略手段。

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10、

Flexible Recommendation System consists of database, models warehouse, recommendation strategies, recommendation control engine, recommendation manager and the website.

FRS主要由推荐系统数据库、推荐系统模型库、推荐策略、推荐控制引擎、推荐管理和电子商务网站系统组成。

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11、
12、

First, User Modeling collects user personal data, and provides user's interests and preference to Recommendation System.

首先,用户建模是收集用户数据,目的是为推荐系统提供用户的兴趣、偏好等建立模型;

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13、

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.

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。

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14、

The Structure and Theory of Personalized TV Program Recommendation System

个人电视节目推荐系统的结构与原理

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15、

Digital TV program recommendation system based on explicit user profiles

基于显性用户特征的数字电视节目推荐系统

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16、

The experiment shows that interestingness could reduce the number of rules while no interestingness could achieve optimal result for all data sets. Lastly, associative classification is used in electronical recommendation system.

实验结果表明兴趣度可减少规则的数量但没有一个兴趣度对于所有数据集都能够达到最优的分类效果。最后,本文将关联分类应用于电子商务推荐系统中。

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17、

Those emerging technologies provide a new way for the design of recommendation system.

这些新兴技术的出现为推荐系统的设计提供了新的思路。

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18、

Online Book Recommendation System of University Library Based on Hibernate

基于Hibernate框架的高校图书馆网上荐购系统

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19、

The current personalized recommendation system is based on the correlation ship between users and products, that is, it predicts the interestingness of users to products according to the existing consuming behavior or the evaluations and recommend personally.

当前个性化推荐系统是基于用户和产品之间的相关关系,即通过用户已有的购买行为或对产品的评价来预测用户对待推荐产品的兴趣度,进而进行个性化商品推荐。

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20、

Research on Agent-Based Personalized Recommendation System

基于Agent的个性化推荐系统的研究

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