文章基本信息

标题：Comparison of Collaborative Filtering Algorithms with Various Similarity Measures for Movie Recommendation
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作者：Taner Arsan ; Efecan Koksal ; Zeki Bozkus 等
期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2016
卷号：6
期号：3
DOI：10.5121/ijcsea.2016.6301
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Collaborative Filtering is generally used as a recommender system. There is enormous growth in the amount of data in web. These recommender systems help users to select products on the web, which is the most suitable for them. Collaborative filtering-systems collect user's previous information about an item such as movies, music, ideas, and so on. For recommending the best item, there are many algorithms, which are based on different approaches. The most known algorithms are User-based and Item-based algorithms. Experiments show that Item-based algorithms give better results than User-based algorithms. The aim of this paper isto compare User-based and Item-based Collaborative Filtering Algorithms with many different similarity indexes with their accuracy and performance. We provide an approach to determine the best algorithm, which give the most accurate recommendation by using statistical accuracy metrics. The results are compared the User-based and Item-based algorithms with movie recommendation data set.
关键词：Collaborative Filtering; Recommendation Systems; User-based Algorithms; Item-based Algorithms