Information Gain Clustering through Roulette Wheel Genetic Algorithm (IGCRWGA): A Novel Heuristic Approach for Personalisation of Cold Start Problem

Abstract

Information Gain Clustering through Roulette Wheel Genetic Algorithm (IGCRWGA) is a novel heuristic used in Recommender System (RS) for solving personalization problems. In a bid to generate information on the behavior and effects of Roulette Wheel Genetic Algorithm (RWGA) in Recommender System (RS) used in personalization of cold start problem, IGCRWGA is developed and experimented upon in this work / paper. A comparison with other heuristics for personalization of cold start problem - such as Information Gain Clustering Neighbor through Bisecting K-Mean Algorithm (IGCN), Information Gain Clustering through Genetic Algorithm (GCEGA), among others -- showed that IGCRWGA produced the best recommendation for large recommendation size (i.e. greater than 30 items) since it is associated with the least Mean Absolute Error (MAE), the evaluation metric used in this work.