期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2014
卷号：6
期号：2
页码：51
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Recommender systems provide useful recommendations to a collection of users for items or products thatmight be of concern or interest to them. Several techniques have been proposed for recommendation suchas collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of thesetechniques suffers from scalability, data sparsity, and cold-start problems when applied individuallyresulting in poor recommendations. This paper proposes an adaptive hybrid recommender system thatcombines multiple techniques together to achieve some synergy between them. Collaborative filtering anddemographic techniques are combined in a weighted linear formula. Different experiments applied usingmovieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknessesresulted when using traditional recommendation techniques.
