Recommendation Algorithms: A Comparative Study in Movie Domain

TL;DR

This paper compares various recommendation algorithms in the movie domain using the Netflix dataset, highlighting the effectiveness of matrix factorization methods in improving recommendation accuracy.

Contribution

It introduces a regression-based approach with novel features for movie recommendation and evaluates multiple algorithms on the Netflix dataset.

Findings

Matrix Factorization algorithms achieved the lowest RMSE.

Feature engineering significantly impacts recommendation accuracy.

Regression models with novel features outperform traditional methods.

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.