Movie genre preference prediction using machine learning for customer-based information

Abstract

Abstract-Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers' demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik-Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers' preferences with a small data set and design prediction tools for these enterprises.

Single and Ensemble Classification for Predicting User’s Restaurant Preference

“…From the table it can be observed that DT and NB classifiers generated the same and the highest classification accuracy (Accuracy= 86.92 and AUC = 0.98). Table) 72 Because the Ensemble model effectiveness is highly affected by the base classifiers [11], the Ensemble classification experiments were only conducted using DT and Naïve Bayes classifiers as base classifiers. Table III presents the obtained results from using ensemble classification to generate the RCPPM.…”

“…The last step in the classification process is the model usage, where the prediction model is utilized to predict class labels for new unseen data. Classification has been employed in many application domains, examples of application domains include: text categorization [4], bioinformatics [5], manufacturing [6], e-learning evaluation system [7], medical diagnosis [8], data management [9], music categorization [10] and movie genre prediction [11]. Among these music categorization and movie genre predictions or genre preferences prediction [12], [13] could be considered as entertainment applications of classification.…”

Utilizing Imbalanced Data and Classification Cost Matrix to Predict Movie Preferences

“…Researchers usually categorize recommender systems into collaborative filtering [15] and content-based filtering systems [11,16,17]. A brief review of both filtering methods and known issues associated with the approaches have been summarized in [9]. The proposed work here can be viewed as a continuation of the works in [9].…”

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“…For the sake of simplicity, we only regarded the scale of 4 and 5 as a positive recommendation and other scales were negative ones. The selection of a full set of features in the dataset is explained in [9]. And we do not train the proposed model with other information such as actor information provided in…”