Modeling relationships between retail prices and consumer reviews: A machine discovery approach and comprehensive evaluations

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{YANG:2021:DSS,

• author = "Xian Yang and Guangfei Yang and Jiangning Wu and Yanzhong Dang and Weiguo Fan",
• title = "Modeling relationships between retail prices and consumer reviews: A machine discovery approach and comprehensive evaluations",
• journal = "Decision Support Systems",
• volume = "145",
• pages = "113536",
• year = "2021",
• ISSN = "0167-9236",
• DOI = "doi:10.1016/j.dss.2021.113536",
• URL = "https://www.sciencedirect.com/science/article/pii/S0167923621000464",
• keywords = "genetic algorithms, genetic programming, Consumer reviews, Retail price, Data-driven, Machine learning, Product involvement",
• abstract = "Setting the retail price as a part of marketing would affect customers' cognition regarding products and affect their post-purchase behavior of review writing. To deeply understand the relationships between retail prices and reviews, this paper designs an intelligent data-driven Generate/Test Cycle using a machine learning technique to automatically discover the relationship model from a huge amount of data without a prior hypothesis. From a unique dataset, various free-form relationship models with their own structures and parameters have been discovered. By the comprehensive evaluations of candidate models, a guided map was offered to understand the relationship between dynamic retail prices and the volume/valence of reviews for different types of products. Experimental results show that 37.69percent of products in our sample exhibit the following trend: When the price is increased to a certain level, the volume of reviews shifts from a decreasing trend to an increasing trend. Results also demonstrate that a linearly increasing relationship model between prices and the valence of reviews is more suitable for the low-involvement products than for the high-involvement products. In addition to the new findings, this research provides a powerful tool to assist domain experts in building relationship models for decision making in a highly efficient manner",

}

Genetic Programming entries for Xian Yang Guangfei Yang Jiangning Wu Yanzhong Dang Weiguo Fan

Citations