Decision Model for Market of Performing Arts with Factorization Machine

作     者：徐勇 唐倩 侯林早 李冕 

作者机构：Shanghai United Media GroupShanghai 200041China University of Michigan-Shanghai Jiao Tong University Joint InstituteShanghai Jiao Tong UniversityShanghai 200240China 

基　　金：the Fund of the Science and Technology Commission of Shanghai Municipality(No.13511506402) 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2018年第23卷第1期

页      码：74-84页

摘      要：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.

主 题 词：performing arts factorization machine(FM) Markov chain Monte Carlo(MCMC) adaptive stochastic gradient descent(ASGD) 

学科分类：081203[081203] 08[工学] 0835[0835] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1007/s12204-018-1912-2

馆 藏 号：203284754...