Decision Model for Market of Performing Arts with Factorization Machine

Abstract

Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However, they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine (FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent (ASGD) and Markov chain Monte Carlo (MCMC) are both explored to estimate the model parameters of FM. FM with ASGD (FM-ASGD) and FM with MCMC (FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models’ repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.