  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.
