Information Theoretic Approach to Cold Start Problem Using Genetic Algorithms

Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and new customer personalization is one of these problems. In this paper, a method combining GA based clustering algorithm with Collaborative Filtering CF-based Recommender system is proposed named Information Gain Clustering using Genetic Algorithm (IGCGA), which alleviates the problem of being trapped in local clustering centroids using k-mean. Simulation results show that the proposed IGCGA, in most of the cases, is able to find much accurate personalization of new users compared to IGCN other Collaborative Filtering based Recommender system. Much better performance of IGCGA is observed.