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https://hdl.handle.net/11147/5180
Title: | Performance comparison of combined collaborative filtering algorithms for recommender systems | Authors: | Tapucu, Dilek Kasap, Seda Tekbacak, Fatih |
Keywords: | Recommender systems Collaborative filtering Algorithms Data handling Combined solution |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Source: | Tapucu, D., Kasap, S., and Tekbacak, F. (2012, July 16-20). Performance comparison of combined collaborative filtering algorithms for recommender systems. Paper presented at the 36th Annual IEEE International Computer Software and Applications Conference Workshops. doi:10.1109/COMPSACW.2012.59 | Abstract: | Recommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values. © 2012 IEEE. | Description: | 36th Annual IEEE International Computer Software and Applications Conference Workshops, COMPSACW 2012; Izmir; Turkey; 16 July 2012 through 20 July 2012 | URI: | http://doi.org/10.1109/COMPSACW.2012.59 http://hdl.handle.net/11147/5180 |
ISBN: | 9780769547589 | ISSN: | 0730-3157 |
Appears in Collections: | Computer Engineering / Bilgisayar Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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