Title: Hybrid Recommender Systems

Abstract

E-commerce companies have developed tools to help users in searching the most suitable product for their needs. The most successful and widely used tool in this area has been the Recommender Systems. Personal recommender systems can also help the customers on relevant products by finding the past purchasing history, customer interest profile and product features. However, sometimes recommendations generated by these systems are not satisfactory. There are various techniques have been implemented to generate the useful recommendations for their customers. Hybrid filtering method combines collaborative and content-based methods or combines collaborative and knowledge based methods in order to overcome cold-start or ramp-up problems and performances issues.