Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems

Abstract

Recommender systems aim to help web users to find only close information to their preferences rather than searching through undifferentiated mass of information. Currently, collaborative filtering is probably the most known and commonly used recommendation approach in recommender systems. In this paper, we present a new genetic algorithms-based recommender system, SimGen, that computes the similarity values between users without using any of the well-known similarity metric calculation algorithms like Pearson correlation and vector cosine-based similarity. The results obtained present 46% and 38% improvements in prediction quality and performance, respectively when compared with other techniques.