Analysis of Movie Recommendation Systems; with and without considering the low rated movies

Movie recommendation system is one of the top research areas, currently. Due to the impact of high internet speeds, multimedia has become one of the best entertainments. Recommendation system has its applications like movie recommendations, course recommendations, e-commerce etc.. Movie recommendation system scope is not limited to entertainment, but also in information sharing. Movie recommendation systems suffer from problems like Cold-start problem, Sparsity, Long-tail problem, Grey sheep problem etc.. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with and without considering the movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering” and the similarity measure used is the “Pearson correlation coefficient”. Dataset considered is Movie-Lens-100k. This experiment result shows that low rated movies are not significant in finding the movie predictions. So it’s suggestable to ignore them while calculating movie predictions.