Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Forecasting box office revenue of movies with BP neural network
Expert Systems with Applications: An International Journal
Improving Movie Gross Prediction through News Analysis
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Predicting box-office success of motion pictures with neural networks
Expert Systems with Applications: An International Journal
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Predicting friendship links in social networks using a topic modeling approach
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Why watching movie tweets won't tell the whole story?
Proceedings of the 2012 ACM workshop on Workshop on online social networks
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In the recent past, machine learning algorithms have been used effectively to identify interesting patterns from volumes of data, and aid the decision making process in business environments. In this paper, we aim to use the power of such algorithms to predict the pre-release box-office success of motion pictures. The problem of forecasting the box-office collection for a movie is reduced to the problem of classifying the movie into one of several categories based on its revenue. We propose a novel approach to constructing and using a graph network between movies, thus alleviating the movie independence assumption that traditional learning algorithms make. Specifically, the movie network is first used with a transductive algorithm to construct features for classification. Subsequently, a classifier is learned and used to classify new movies with respect to their predicted box-office collection. Experimental results show that the proposed approach improves the classification accuracy as compared to a fully independent setting.