随着网络的迅速发展,各种数据量变得庞大且分散,利用关键词检索数据的传统方式变得相当费时。为了减少用户在网络上的搜寻时间,提供用户更确切的内容信息,自动化推荐系统(Automatic Recommender System)应运而生。该研究将人工神经网络中的自适应共振理论(Adaptive Resonance Theory,ART)和数据挖掘技术结合起来,建构了一个可自动聚类族群特征且能挖掘出关联规则的自动化在线推荐机制。同时将用于用户聚类的ART算法进行了改进,提出了MART聚类算法,使由推荐系统得出的结果变得更加合理和灵活。
Abstract
With the rapid development of Internet, a large number of data of various type become huge and scattered. Using traditional keyword to search the data is more and more time-consuming. Therefore, the automatic recommender system emerges to reduce users search time and provide them with more appropriate information, . By using ART neural network and data mining technology, this study builds a typical online recommendation system. It can automatically cluster population characteristics and mine the associated characteristics. At the same time, MART algorithm is proposed as a modified ART algorithm for clustering algorithm, which produces more reasonable and flexible clustering results.
Key wordsthe automatic recommender system; adaptive resonance theory; data mining technology; association rules
关键词
自动化推荐系统 /
自适应共振理论 /
数据挖掘 /
关联规则
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Key words
the automatic recommender system /
adaptive resonance theory /
data mining technology /
association rules
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参考文献
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脚注
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基金
浙江省自然科学基金(Y1110649)
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