20 years of MovieLens datasets have witnessed a blossom of research that is garnering a remarkable significance with the advent of e-commerce and the whole industry. Four variations of the dataset have been downloaded hundreds of thousands of times, reflecting their popularity and distinctive contribution in the field of recommendation systems and connected subjects. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags, or tag interpolation. The problem has not been addressed in the literature, especially for the 100K variations of the MovieLens datasets. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our model has eliminated the e.ect of the cold-start problem. The experimental results provide us much better recommendations to users because it utilizes the underlying relation between movies based on their similar genres and actors/actresses. A movie recommender system has been deployed to demonstrate our work.