A ranking based recommender system for cold start & data sparsity problem

Recommender Systems have been very common and useful nowadays, for predictions of different items which facilitate user by giving suitable recommendations. It deals with the specific type of items and technique used to generate the recommendations that are customized to provide valuable and effective suggestions to the end user. The present system considers two well-known problems during recommendation such as cold start and data sparsity and resolved these problems to the great extend with high accuracy. The proposed system provides the recommendation to new user, with high reliability and accuracy values as shown in our result.