基于数据挖掘的自动化推荐系统改进ART算法探究

Research on the Improvement of ART Algorithms in Automated Recommendation System Based on Data Mining

随着网络的深度普及,人们对于互联网的使用越来越频繁,随之而来的是庞大且分散的各式各样的数据。面对海量的数据,用户在查询时往往难以高效地获取到对自己有价值的信息。为了向用户提供更精准的信息,提高用户获取有效信息的效率,自动化推荐系统适时而生。不管是通信领域还是其他领域,繁杂的数据都需要一种优异的处理算法或者机制来进行处理,本文通过把自适应共振理论(ART)和数据挖掘技术两者结合,形成一个自动化在线推荐系统,并对用于用户聚类的ART算法进行改进,提升了通信领域中推荐系统推荐的合理性与有效性。

With the popularization of the internet,people become more and more dependent on the use of the internet. Faced with massive data,it is often difficult for users to efficiently obtain valuable information for themselves when querying. In order to provide users with more accurate information and improve the efficiency of users to obtain effective information,the automatic recommendation system is timely born. Both communication and other areas,complex data needs to be an excellent processing algorithms or mechanism to deal with,in this paper,the adaptive resonance theory(ART) and a combination of data mining technology,forming an automated online recommendation system,and for users to improve the ART of clustering algorithm,improved communication in the field of recommendation system recommended by the rationality and validity.