Incorporating Preference Changes through Users’ Input in Collaborative Filtering Movie Recommender System

The usefulness of Collaborative filtering recommender system is affected by its ability to capture users' preference changes on the recommended items during recommendation process. This makes it easy for the system to satisfy users' interest over time providing good and quality recommendations. The Existing system studied fails to solicit for user inputs on the recommended items and it is also unable to incorporate users' preference changes with time which lead to poor quality recommendations. In this work, an Enhanced Movie Recommender system that recommends movies to users is presented to improve the quality of recommendations. The system solicits for users' inputs to create a user profiles. It then incorporates a set of new features (such as age and genre) to be able to predict user's preference changes with time. This enabled it to recommend movies to the users based on users new preferences. The experimental study conducted on Netflix and Movielens datasets demonstrated that, compared to the existing work, the proposed work improved the recommendation results to the users based on the values of Precision and RMSE obtained in this study which in turn returns good recommendations to the users.