Kernel based collaborative recommender system for e-purchasing

Recommender system a new marketing strategy plays an important role particularly in an electronic commerce environment. Among the various recommender systems, collaborative recommender system (CRS) is widely used in a number of different applications such as recommending web pages, movies, tapes and items. CRS suffers from scalability, sparsity, and cold start problems. An intelligent integrated recommendation approach using radial basis function network (RBFN) and collaborative filtering (CF), based on Cover's theorem, is proposed in order to overcome the traditional problems of CRS. The proposed system predicts the trend by considering both likes and dislikes of the active user. The empirical evaluation results reveal that the proposed approach is more effective than other existing approaches in terms of accuracy and relevance measure of recommendations.