Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation

Abstract

Nowadays, e-commerce platforms, such as Amazon, Flipkart, Netflix and YouTube, extensively use recommender systems (RS) techniques. Collaborative filtering (CF) is used widely among all RS techniques. A CF analyzes the user’s preference from past data, like ratings, and then suggests actual items to the intended user. The existing techniques compute the similarity between users/items and predict the ratings. However, most of them indicate the user’s preference for the items using a single technique, which may produce poor results. This paper proposes a hybrid CF technique to enhance the movie recommendation (HCFMR). The HCFMR consists of two modules. The first module finds the prediction score with the help of matrix factorization (MF) and passes the prediction score as input to the prediction algorithm, i.e., extreme gradient boosting (XGBoost). The second module generates handcrafted features, such as similar users and movies, along with the user, item and global average. Finally, these features are supplied to the XGBoost to predict the rating score of the movie and recommend the topmost movie to the user. We conduct various simulations on real-world datasets to verify the effectiveness of the proposed technique against the baseline techniques. The exploratory outcomes signify that the HCFMR technique outperforms the baselines and provides a better prediction on the benchmark datasets.