Building Accurate and Practical Recommender System Algorithms Using Machine Learning Classifier and Collaborative Filtering

Recommender systems use machine learning and data mining techniques to filter unseen information and predict whether a user would like a particular item. A major research challenge in this field is to make useful recommendation from available set of millions of items with sparse ratings. A large number of approaches have been proposed aiming to increase accuracy, but they have ignored potential problems, such as sparsity and cold start problems. From this line of research, in this research work, we have proposed a novel hybrid recommendation framework that combines content-based filtering with collaborative filtering that overcome aforementioned problems. Our experimental results show that this performance of proposed algorithm is better or comparable with the individual content-based approaches and naive hybrid approaches, while it eliminates various problems faced by recommender systems.