Survey Paper on Web Recommendation System

The internet has become a significant source of information. it has provide platform for many e-commerce enterprises. These e-commerce sites have very broad variety of products and have loads of information about the product or services so it is difficult for the customers to choose best product according to their needs. To overcome the problem of information overload, many Recommendation techniques have been proposed earlier. A personalized recommendation system can handle this issue. There are still challenges in Recommender system 1) The items and user profiles in e-commerce sites such as e-learning, e-business are so complex and vague so they can be described as complex tree structure. Previous.2) Attributes of items and user behavior are subjective, vague and imprecise. These in turn induce uncertainty in representing and reasoning on items features, users behavior, and their relationships so fuzzy set theory is used to handle this uncertainty. An item tree and user request tree-based hybrid recommendation approach is then developed. To model users fuzzy tree-structured preferences, a fuzzy preference tree model is proposed. A fuzzy preference tree-based recommendation approach is then developed. Experimental results on an Australian business dataset and the Movie lens dataset show that the proposed recommendation approach have good performance and handled tree-structured data Efficiently.