Personalized Movie Recommendation System with User Interest in Social Network

Recommender systems have grown extremely in day to day life, and it is very useful in a plenty of applications. Although the recommender system is very popular, there are some serious problems like cold start (unavailability of data for modeling algorithms) and sparsity (initial rating not available). This made people to step back in analyzing the functionality of those algorithms which leads to little decrease in creativity and optimizations in data mining algorithms and recommendation system. With the changed people and excessive growth of social network, people are accepting to share their personal interests via ratings, reviews, and likes on social networks like facebook, twitter etc. To recommend user interested movie and to solve the cold start and sparsity problem of input data, we develop Personalized Movie Recommendation System (PMRS). In PMRS model we used personal interest, similarity interest, and trust value to forms an optimized method of recommendation system.