Feature Engineering in Hybrid Recommender Systems

The increasing growth of the World Wide Web especially in a social network with the multiplicity of items offered (such as products or web pages), it is really difficult for a user to pick up relevant items who is searching for it. On the other hand, the different tastes and behaviors of users is making the probability for finding a neighbor user hard to get, therefore, difficult for automated software systems to discover what is interesting to the user. We have proposed a new approach to adapt to this widespread in e-commerce nowadays and reduce the impact of the multiplicity of items and different views of the users that can quickly produce the recommendations through, exploit the domain knowledge of training data set to create testing data set depending on attributes of one feature that represents the distinctive genres of item as the inputs to a hybrid recommender systems which is aspired to achieve best recommendations by implementing metalevel hybridization techniques that combine of collaborative recommender systems and content-based recommender systems, these operations will reduce from the effects of sparsity, cold start and scalability very common problems with the collaborative recommender systems additional to improve the accuracy of recommendation comparing with the pure collaborative filtering Pearson Correlation approach.