IGCEGA: A Novel Heuristic Approach for Personalisation of Cold Start Problem

摘要

IGCEGA, an acronym for Information Gain Clustering through Elitizt Genetic Algorithm, is a novel heuristic used in Recommender System (RS) for solving personalization problems. In comparison with IGCGA (Information Gain Clustering through Genetic Algorithm), IGCEGA is not associated with the inherent problem of increasing the possibility of losing good solution during the crossover phase, which translates into increasing the guarantee of converging to a global minima and consequently, enhancing the accuracy of the recommendation. Besides, IGCEGA using the technique of global minima still resolves the problem associated with IGCN (Information Gain through Clustered Neighbor), which traps the algorithm in local clustering centroids. Although this problem was alleviated by IGCGA, IGCEGA solves the problem even better because IGCEGA assumes the lowest Mean Absolute Error (MAE), the evaluation matrix used in this work. Results of the experimentation of the various heuristics / techniques in RS used in personalization for cold start problems -- for instance Popularity, Entropy, IGCN, IGCGA - showed that IGCEGA is associated with the lowest MAE, therefore, best clustering, which in turn results into best recommendation.