🤖 AI Summary
This study addresses the limited personalization performance in movie recommendation systems by formulating the recommendation task as a regression problem. Leveraging the Netflix dataset, the authors conduct an exploratory analysis that integrates multi-source features, including aggregated statistics, matrix factorization embeddings, and user–item similarity measures. They innovatively combine these heterogeneous feature types into unified regression models and systematically compare the predictive performance of XGBoost, k-nearest neighbors (KNN), and matrix factorization (implemented via the Surprise library). Using root mean squared error (RMSE) as the evaluation metric, experimental results demonstrate that matrix factorization achieves the best performance on this dataset, thereby validating both the effectiveness of the proposed feature fusion strategy and the practical utility of comparative algorithmic evaluation in recommender systems.
📝 Abstract
Intelligent recommendation systems have clearly increased the revenue of well-known e-commerce firms. Users receive product recommendations from recommendation systems. Cinematic recommendations are made to users by a movie recommendation system. There have been numerous approaches to the problem of recommendation in the literature. It is viewed as a regression task in this research. A regression model was built using novel properties extracted from the dataset and used as features in the model. For experimentation, the Netflix challenge dataset has been used. Video streaming service Netflix is a popular choice for many. Customers'prior viewing habits are taken into account when Netflix makes movie recommendations to them. An exploratory data analysis on the Netflix dataset was conducted to gain insights into user rating behaviour and movie characteristics. Various kinds of features, including aggregating, Matrix Factorization (MF) based, and user and movie similarity based, have been extracted in the subsequent stages. In addition to a feature in the XGBoost regression algorithm, the K-Nearest Neighbors and MF algorithms from Python's Surprise library are used for recommendations. Based on Root Mean Square Error (RMSE), MF-based algorithms have provided the best recommendations.