A Survey on Personalized Endorsement Approaches in Web Usage Mining

Web surfing has become a popular activity for many users who not only make purchases online, but also seek relevant information on products and services before they commit to buy or use. Personalized recommendations (PR) in web usage mining were the way to reach a product to a customer which they really interested. PR will provide appropriate products or services to customers which they really interested. In fact PR has several advantages; it also suffers from some problems like local information overload. In present, PR to each customer is really difficult one due to huge network traffic. This paper surveys various PR methods and algorithms, which used to reach a product to a customer in a fast and efficient way.