Recency augmented hybrid collaborative movie recommendation system

As days rolls internet information grows exponentially and the personalized recommendation system has its vibrant area of research providing relevant information to the user needs. Hybrid recommender systems combines both implicit and explicit feedbacks from user by integrating collaborative and content based recommender system. However, the real time hybrid systems are not focusing on current temporal context of the user on providing the recommendation. This paper proposed a hybrid movie recommendation system that considers recent transactions and also demographic attributes for recommending a item to the target user. The demographic attributes aids in overcoming the cold start problem. The results of the experimental study on movie lens dataset clearly indicate that the proposed system was found to be effective by considering the recent transactions with higher ratings.