文章基本信息

标题：A Hybrid Online Genre-based Recommender System
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作者：Dr. Amit Verma ; Harpreet Kaur Virk
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2015
卷号：6
期号：3
页码：113-116
语种：English
出版社：Ayushmaan Technologies
摘要：Recommendation is a sort of web intelligence technique used for filtering information to provide relevant data to the people on daily basis. There are various approaches used for recommendation. In content-based filtering, the systems examine items previously chosen by the actual user, whereas in collaborative filtering, recommendations are based on the information of similar users or items. The recommendations are also influenced by the factors such as age, gender and some other user profile information. In this paper, both content and collaborative techniques along with some demographic information are combined into a hybrid approach, where additional content features are used to improve the accuracy of collaborative filtering. And the genetic algorithm along with kNN approach and correlation is used to provide recommendations to the user. The system is evaluated using precision and recall parameters.
关键词：Clusters;Classification;kNN;Demographic Information etc