Comparison and Improvement Of Collaborative Filtering Algorithms

Abstract: Recommender Systems is a topic several computer scientists have researched. With today’s e-commerce and Internet access, companies try to maximize their profit by utilizing var- ious recommender algorithms. One methodology used in such systems is Collaborative Filtering. The objective of this paper is to compare four algorithms, all based on Collaborative Filtering, which are k-Nearest-Neighbour, Slope One, Singular Value Decomposition and Average Least Square algorithms, in order to find out which algorithm produce the best pre- diction rates. In addition, the paper will also use two mathematical models, the Arithmetic Median and Weighted Arithmetic Mean, to determine if they can improve the prediction rates. Singular Value Decomposition performed the best out of the four algorithms and Aver- age Least Square performed the worst. However, the Arithmetic Median performed slightly better than Singular Value Decomposition and the Weighted Arithmetic Mean performed the worst.