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 satisfy interest over time providing good and quality recommendations. Existing studied fails solicit user inputs also unable incorporate with which lead poor In this work, an Enhanced Movie Recommender that recommends movies users presented improve solicits create a profiles. It then incorporates set new features (such as age genre) be able predict user's time. enabled recommend based preferences. experimental study conducted Netflix Movielens datasets demonstrated that, compared existing proposed work improved results values Precision RMSE obtained in turn returns recommendations users.