Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems

Abstract

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.

Speeding Up Recommender Systems Using Association Rules

“…The results show that using expert users in an item-based collaborative filtering algorithm has increased the speed of generating recommendations with preserving the accuracy to be very close to original results. Tapucu et al [11] carried out some experiments to check the performance of user-based, item-based, and combined user/itembased collaborative filtering algorithms. Different aspects have been considered in their comparisons, such as size and sparsity of datasets, execution time, and k-neighborhood values.…”