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.

翻译：智能推荐系统显著提升了知名电子商务企业的收入。用户通过推荐系统获得产品推荐。电影推荐系统则为用户提供影视内容推荐。文献中已存在多种解决推荐问题的方法。本研究将其视为回归任务进行处理。通过从数据集中提取新颖特征作为模型输入，构建了回归模型。实验采用Netflix挑战赛数据集。流媒体服务平台Netflix是广受欢迎的服务提供商，其电影推荐机制会综合考虑用户的历史观影记录。通过对Netflix数据集进行探索性数据分析，深入理解了用户评分行为与电影特征。后续阶段提取了多种特征类型，包括聚合特征、基于矩阵分解（MF）的特征以及基于用户与电影相似度的特征。除采用XGBoost回归算法进行特征建模外，还使用Python Surprise库中的K近邻算法与MF算法进行推荐生成。基于均方根误差（RMSE）评估指标，基于MF的算法产生了最优的推荐效果。