Movie Popularity Classification Based on Support Vector Machine Combined with Social Network Analysis

• 영문명: Movie Popularity Classification Based on Support Vector Machine Combined with Social Network Analysis
• 발행기관: 한국IT서비스학회
• 저자명: Tserendulam Dorjmaa Taeksoo Shin
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제16권 제3호, 167~183쪽, 전체 17쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2017.09.30

영문 초록

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie’s genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies’ network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie’s network. Those four centrality values and movies’ genre data were used to classify the movie popularity in this study. The logistic regression, neural network, naïve Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier’s performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie’s genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie’s genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

목차

1. Introduction

2. Related Work

3. Research Mode

4. Experiments and Results

5. Conclusion

키워드

Movie Popularity Classification Social Network Analysis Movie Popularity SVM