用回归模型分析影评数据，提升电影推荐精准度
Recommendation Algorithms: A Comparative Study in Movie Domain
- 将推荐问题建模为回归任务，融合多种特征提取方法
- 基于Netflix数据集，矩阵分解算法在RMSE上表现最佳
- 适合对推荐系统机制感兴趣的工程师和研究者
智能推荐系统显著提升了知名电商企业的收入。本研究聚焦电影推荐，将其视为回归任务。通过探索性数据分析，从Netflix挑战数据集提取了包括聚合特征、基于矩阵分解（MF）的特征以及用户与影片相似性特征。实验中结合XGBoost回归模型，并引入Python Surprise库中的K近邻与MF算法进行推荐。结果表明，在均方根误差（RMSE）指标下，基于矩阵分解的算法表现最优，验证了其在电影推荐场景中的有效性。
原文摘要 · Abstract (English)
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.
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