Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - Scopus: 5Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems(Institute of Electrical and Electronics Engineers Inc., 2012) Tapucu, Dilek; Kasap, Seda; Tekbacak, FatihRecommender 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.Conference Object Citation - WoS: 6Citation - Scopus: 11Dynamically Adaptive Partition-Based Data Distribution Management(Institute of Electrical and Electronics Engineers Inc., 2005) Kumova, Bora İsmailPerformance and scalability of distributed simulations depends primarily on the effectiveness of the employed data distribution management (DDM) algorithm, which aims at reducing the overall computational and messaging effort on the shared data to a necessary minimum. Existing DDM approaches, which are variations and combinations of two basic techniques, namely region-based and grid-based techniques, perform purely in the presence of load differences. We introduce the partition-based technique that allows for variable-size partitioning shared data. Based on this technique, a novel DDM algorithm is introduced that is dynamically adaptive to cluster formations in the shared data as well as in the physical location of the simulation objects. Since the re-distribution is sensitive to inter-relationships between shared data and simulation objects, a balanced constellation has the additional advantage to be of minimal messaging effort. Furthermore, dynamic system scalability is facilitated, as bottlenecks are avoided.