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- 저자 : Tserendulam Dorjmaa (검색결과 5 건)
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Tserendulam Dorjmaa,Taeksoo Shin 한국경영학회 2015 한국경영학회 통합학술발표논문집 Vol.2015 No.08
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 many fields such as movie recommendation of e-commerce service. However, most of classification approaches for predicting 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, naive 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 has about 10% higher accuracy than other classification models. The implications of our results show that our proposed model could be used for improving movie popularity classification.
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Tserendulam Dorjmaa,Taeksoo Shin 한국IT서비스학회 2017 한국IT서비스학회지 Vol.16 No.3
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.
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Evaluating the Quality of Recommendation System by Using Serendipity Measure
Tserendulam Dorjmaa,신택수 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.4
Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.
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