Ontology-based Recommender System with Descriptive Analytics in e-Commerce

The amount of information and users has been increasing at a remarkable rate in recent years. This is where the recommender system comes in, recommender system is a system that generates a list of recommended products for the user. Recommender system has outshined as one of the important features in an e-Commerce portal. Several recommender techniques have been proposed, yet, problems such as cold-start item problem, cold-start user problem and data sparsity problem still existed. The expected outcomes of this paper are an ontology-based recommender system, combining the collaborative and content-based filtering approaches. In addition, the recommender system also generates recommendations by considering user preferences based on social network association. The experimental evaluation indicated that the proposed approach works more efficiently and effectively improves the recommendation.