A framework for a recommendation system based on collaborative filtering and demographics

Recommendation systems attempt to predict the preference or rating that a user would give to an item. Knowledge discovery techniques can be applied to the problem of making personalized recommendations about items or information during a user´s visit to a website. Collaborative Filtering algorithms give recommendations to a user based on the ratings of other users in the system. Traditional collaborative filtering algorithms face issues such as scalability, sparsity and cold start. In the proposed framework, prediction using item based collaborative filtering is combined with prediction using demographics based user clusters in an adaptive weighted scheme. The proposed solution will be scalable while addressing user cold start.