Recommender system using fuzzy c-means clustering and genetic algorithm based weighted similarity measure

A combinatorial approach by combining fuzzy c-means clustering technique and genetic algorithm based weighted similarity measure to develop a recommender system (RS) has been presented in this paper. The approach is carried out for the development of collaborative filtering based recommender system. In this approach fuzzy c-means clustering technique has been applied to the dataset and clustering is done on the basis of the ratings provided by the users in the dataset. These cluster values are then passed to the genetic algorithm based weighted similarity measure to find the similarity between the clustered values and obtaining optimal similarity metrics. We have also computed the quality measures of the recommender system on the basis of the number of similar values retrieved with respect to the number of iteration runs performed by an algorithm. The results obtained show an improvement in the quality of the recommender system.