ISBN: 978-981-11-0008-6 DOI: 10.18178/wcse.2016.06.121
Selecting Classification Model for the Personalized Movie Recommendation System by Feature Adjustment Method
Abstract— Recommendation systems are widely used to improve market potential in theater business today.
However, the efficiency of the personalized movie recommendation system (PMRS) using model-based
techniques is related to employed classifier and a number of features. This research aims to select a suitable
classification model by feature adjustment method for creating the recommendation rules of PMRS. The
suggestion model is appraised using retrieval performance measures by Accuracy between 3 algorithms of
classification consisting of J48, Naïve Bayes (NB) and Multilayer Perceptron (MLP). The datasets for model
construction are collected through surveying from 383 movie audiences who live in Nakhon-Ratchasima
province, Thailand. The results of the accuracy performance show that J48 algorithm produces the finest
accuracy (70.28%) followed by NB (68.28%) and MLP (66.23%), respectively. In addition, the performance
of J48 by feature adjustment method provides 58 combinations which are created from 6 features of movie
audience’s profile and 19 features of movie genres. The results of feature adjustment method present the
consistency between accuracy performance and a number of features. However, the progress of
recommendation rules set selection for PMRS will be chosen only 36 high performance combinations of
adjustment features and these combinations will be applied to the development of a new personalized movie
Index Terms— personalized recommendation, classification, feature adjustment.
Faculty of Management Science, Nakhon Ratchasima Rajabhat University, THAILAND
School of Information Technology, Suranaree University of Technology, THAILAND
Faculty of Science Technology and Agriculture, Yala Rajabhat University, THAILAND
Cite: Supachanun Wanapu, Thawatphong Phithak, Narodom Kittidachanupap, "Selecting Classification Model for the Personalized Movie Recommendation System by Feature Adjustment Method," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 682-686, Tokyo, 17-19 June, 2016.