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Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz
Author(s) -
Tang Shaojie,
Yuan Jing
Publication year - 2021
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13359
Subject(s) - submodular set function , computer science , profiling (computer programming) , market segmentation , quality (philosophy) , focus (optics) , maximization , segmentation , selection (genetic algorithm) , machine learning , clickstream , artificial intelligence , mathematical optimization , marketing , world wide web , mathematics , philosophy , physics , epistemology , optics , business , operating system , web api , web modeling , web service
Personality quiz is a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e‐commerce sales. We study the problem of how to select and sequence a group of quiz questions so as to optimize the quality of customer segmentation. We assume that the customer will sequentially scan the list of questions. After reading a question, the customer makes two, possibly correlated, random decisions: (i) she first decides whether to answer this question or not, and then (ii) decides whether to continue reading the next question or not. We further assume that the utility of questions that have been answered can be captured by a monotone and submodular function. In general, our problem falls into the category of non‐adaptive active learning‐based customer profiling. Note that under our model, the probability of a question being answered depends on the location of that question, as well as the set of other questions placed ahead of that question, this makes our problem fundamentally different from existing studies on submodular optimization. We develop a series of question selection and sequencing strategies with provable performance bound. Although we focus on the application of quiz design in this study, our results apply to a broad range of applications, including assortment optimization with position bias effect.