Utilizing Imbalanced Data and Classification Cost Matrix to Predict Movie Preferences

TL;DR

This paper develops a movie genre recommendation system that effectively handles imbalanced data and unequal classification costs using ensemble and SVM algorithms, improving prediction accuracy for targeted marketing.

Contribution

It introduces a novel approach combining cost-sensitive learning and ensemble methods to address imbalanced genre preferences in small datasets for SMEs.

Findings

Ensemble methods outperform SVM in accuracy.

Cost-sensitive models improve genre prediction for SMEs.

Selected predictors reduce overfitting and training time.

Abstract

In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include the information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.