Recommendation Algorithms: A Comparative Study in Movie Domain

• URL: http://arxiv.org/abs/2602.24125v1
• Date: Fri, 27 Feb 2026 16:01:10 GMT
• Title: Recommendation Algorithms: A Comparative Study in Movie Domain
• Authors: Rohit Chivukula, T. Jaya Lakshmi, Hemlata Sharma, C. H. S. N. P. Sairam Rallabandi,
• Abstract summary: A regression model was built using novel properties extracted from the dataset and used as features in the model.<n>An exploratory data analysis on the Netflix dataset was conducted to gain insights into user rating behaviour and movie characteristics.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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