Content Based Filtering Techniques in Recommendation System using user preferences

Recommender systems use several of data mining techniques and algorithms to identify user preferences of items in a system out of available millions of choices. Instead of providing a static experience in which users search for and buy products, recommender systems help to increase interaction to provide a richer experience. Recommender systems can easily identify the recommendations autonomously for individual users based on past purchases and searches, and on other users' behaviour.This technique can predict a user’s preferred items by using the user’s past history data as well as other users’ past history data, and then recommends items to the user. This paper is focused on Recommender systems, and its major challenges for instance cold start problem, data sparsity, scalability and accuracy. Content-based filtering constructs a recommendation on the basis of a user's behaviour. As with Collaborative Filtering , the representations of customers’ precedence profile are models which are long-term, and also we can update precedence profile and this work become more available. KeywordsRecommender systems, Collaborative Filtering, Content based Filtering