Multicategory choice modeling with sparse and high dimensional data: A Bayesian deep learning approach

2022 | Decision Support Systems | Citations: 1

Authors: Xia, Feihong; Chatterjee, Rabikar

Abstract: The availability of sparse and high dimensional consumer shopping data poses a c ... Expand

Abstract: The availability of sparse and high dimensional consumer shopping data poses a challenge for researchers for accurate and efficient analysis. While deep learning models can handle such data, most of the results from these models are uninterpretable. This greatly limits their value in applications aimed at better understanding mul­ ticategory shopping behavior and assisting managerial decision-making. Thus, a new approach is needed to analyze high dimensional consumer shopping data in an efficient and interpretable manner, with the potential to greatly strengthen decision support systems for managers especially at large consumer packaged goods firms and giant retailers. We propose a Bayesian deep learning approach based on Variational Autoencoder (VAE) designed to efficiently capture often complex and multifaceted interrelationships across items in the shopping baskets, in the form of substitute and complementary cross effects (which are observable and controllable) and coincidences (which are not). The benefits of the proposed approach are empirically demonstrated by applying our model to a high dimensional supermarket shopping dataset covering very large numbers of products at the stock-keeping unit (SKU) level, households, and shopping trips. We discuss implications for managerial decision-making and identify promising research directions. Collapse

Topics: marketing management decision making open source accounting decision support system

Methods: artificial neural network machine learning deep learning data transformation factor analysis

Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework

2016 | Management Information Systems Quarterly | Citations: 0

Authors: Pil Han, Sang; Park, Sungho; Oh, Wonseok

Abstract: The number of mobile apps launched in the market has exponentially grown to more ... Expand

Abstract: The number of mobile apps launched in the market has exponentially grown to more than 2 million, but little is known about how users choose and consume apps of numerous categories. This study develops a utility theory-based structural model for mobile app analytics. We use the theoretical concepts of utility and satiation along with the factor analytic approach, as bases in simultaneously modeling the complex relationships among choice, consumption, and utility maximization for consumers of various mobile apps. Using a unique panel dataset detailing individual user-level mobile app time consumption, we quantify the baseline utility and satiation levels of diverse mobile apps and delineate how app preferences and consumption patterns vary across demographic groups affected by persistent use and time trends. The findings suggest that users’ baseline utility substantially diverges across app categories and that their demographic characteristics and habit formation explain the appreciable heterogeneity in baseline utility and satiation. These parameters also exhibit positive and negative correlations in mobile websites and app categories. Our modeling approaches and computational methods can unlock new perspectives and opportunities for handling large-scale, micro-level data, while serving as important resources for big data analytics and mobile app analytics. Collapse

Topics: mobile application social network advertising management big data website

Methods: longitudinal research usage data multidimensional scaling descriptive statistic data modeling

Theories: utility theory

Arbitrage pricing theory-based Gaussian temporal factor analysis for adaptive portfolio management

2004 | Decision Support Systems | Citations: 0

Authors: Chiu, Kai-Chun; Xu, Lei

Abstract: Ever since the inception of Markowitz's modern portfolio theory, static portfoli ... Expand

Abstract: Ever since the inception of Markowitz's modern portfolio theory, static portfolio optimization techniques were gradually phased out by dynamic portfolio management due to the growth of popularity in automated trading. In view of the intensive computational needs, it is common to use machine learning approaches on Sharpe ratio maximization for implementing dynamic portfolio optimization. In the literature, return-based approaches which directly used security prices or returns to control portfolio weights were often used. Inspired by the arbitrage pricing theory (APT), some other efforts concentrate on indirect modelling using hidden factors. On the other hand, with regard to the proper risk measure in the Sharpe ratio, downside risk was considered a better substitute for variance. In this paper, we investigate how the Gaussian temporal factor analysis (TFA) technique can be used for portfolio optimization. Since TFA is based on the classical APT model and has the benefit of removing rotation indeterminacy via temporal modelling, using TFA for portfolio management allows portfolio weights to be indirectly controlled by several hidden factors. Moreover, we extend the approach to some other variants tailored for investors according to their investment objectives and degree of risk tolerance. Collapse

Methods: computational algorithm simulation autoregressive conditional heteroscedasticity model factor analysis data transformation

Theories: transaction cost economics