The system can extract the characters of the users to get the eigenvector of every individual and cluster the similar users to get the eigenvector of groups . then these two eigenvectors will be integrated to form the integration eigenvector . the model realizes information filtering by individual filtering , collaborative filtering and integration filtering ( 3 )在过滤技术的选择方面,采用基于个体用户模型的个体过滤、基于小组用户模型的协同过滤和基于综合用户模型的综合过滤这三种过滤技术对教学资源进行过滤。
The article proposes a new multi - models recommendation system ’ s ( mmrs ) design and realization based on b2c . the system deals with server ’ s log , purchasing history , web data and user ' s registration information , uses the associate rule , cluster and a new collaborative filtering method , recommending the result in different ways such as commodities , user ' s comment and email marketing advertisement 本文在该问题基础上提出一种b2c模式下的多模型推荐系统( mmrs )的设计及实现,该系统通过对服务器日志、用户购物历史记录、 web元数据以及用户注册信息处理,运用关联、聚类以及改进的协同过滤方法,最后给出商品、用户评论以及email营销广告等不同方式的推荐结果。
The former one gains the aim of improving the recommended speed by grouping the transaction clusters , which make system has ability to solve the limitations of the collaborative filtering technology which ca n ' t process huge data . the latter one adopts hypergraph partitioning skill and effectively groups the association rules according to the users " historical visiting data 基于关联规则聚类的推荐算法采用超图划分的技术,对根据用户的历史访问数据得到的关联规则进行有效的分组,可以找出不同兴趣用户群的相同访问模式,提高了推荐的质量。
Worth to mention , customer ' s network value has first been used to do segmentation in the research . in the second part , we have proposed a hybrid recommendation strategy . compared with traditional ones , it has combined multi - agent system , collaborative filtering , and a simplified top - n algorithm together 对于第二部分,其主要工作是在完成客户分层的基础上,本文提出了基于多智能主体和协作过滤相结合的高盈利率客户推荐策略,通过智能主体对用户兴趣的分析,结合协作过滤中的群体意见,最终完成推荐。
Finally , we summarize on the paper , point out defects and the directions that will be further studied in the future . in research course , this thesis used web log data from m . com , with the method of user - based collaborative filtering algorithm , and carried out recommendation systems for m . com 在研究过程中,本文通过使用m网站2005年末某日的web日志数据,运用基于user - based协同过滤推荐算法的数据挖掘科学分析方法,使用spss统计工具进行相关性分析,为m网站建立电子商务推荐系统进行的分析和研究。
Collaborative filtering (CF) is a technique used by some recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.