Resumen de Hybrid Measurement of the similarity value based on a Genetic Algorithm to improve prediction in a collaborative filtering recommendation system
Swaleha Zubair, Muaadh Abdo Al Sabri
Recently, a recommendation system (RS) has been widely used in several disciplines, like education, economics, scientific researches and other related fields. Based on the user interface, preferences and constraints the personalized RS has been rated as the most effective method. Collaborative filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. In this filtering method nearest neighbour is identified to a recommendation system. The existing similarity measures use only the co-rated items ratings while calculating the similarities between a pair of users or items. For example, the two standard methods used to measure similarities are cosine similarity (CS) and pearson correlation similarity (PCS). In the present study, we have proposed a model to improve the prediction accuracy of RS using the merge output of CS with PCS based on a Genetic Algorithm (GA). The results show superior features of GA and its ability to achieve more correct predictions as compared to both CS and PCS when used alone.
