Performance comparison of combined collaborative filtering algorithms for recommender systems

36th Annual IEEE International Computer Software and Applications Conference Workshops, COMPSACW 2012; Izmir; Turkey; 16 July 2012 through 20 July 2012Recommender 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