Lean improvement of the stage shows in theme park based on consumer preferences correlation deep mining
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Lean improvement of the stage shows in theme park based on consumer preferences correlation deep mining Shugang Li 1 & Hanyu Lu 1 & Jiali Kong 1,2 & Zhaoxu Yu 3 & Ru Wang 1 Received: 14 July 2019 / Revised: 25 April 2020 / Accepted: 27 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Online comments provide a new and convenient way to understand consumer preference, but these comments for stage shows in theme park are usually incomplete, which can seriously affect the accuracy of existing mining models. In order to overcome the dilemma of missing information, we propose the consumer preferences correlation deep mining model, which precisely mines user preferences from two aspects: comment semantic deep mining and attribute emotion correlation mining. Furthermore, the KanoIPA model is proposed to comprehensively excavate the user satisfaction and the importance of product attributes to give a lean improvement strategy for stage shows. Specifically, firstly, correlation deep mining model is constructed to deeply mine the missing attribute emotional polarity based on the emotional correlation sequence, emotional vector and Senti2vec + Gated Recurrent Unit model. Secondly, correlation width mining model is developed to excavate the user preferences for the stage shows attribute. In the correlation width mining model, the partial regression equation is used to describe the influence of the user emotional polarity on the user satisfaction level. Based on the emotion correlated attribute sequences, the correlation Kano mapping rules are proposed, and then the priority of user preferences for product attributes is given. Thirdly, the KanoIPA model is designed for the lean improvement of products to achieve higher benefits at a lower cost. Finally, the experimental results on Shanghai Disneyland confirm the effectiveness and application value of the proposed model. Consequently, this study provides an accurate decision support model driven by big data for product improvement. Keywords Consumer preferences . Correlation deep mining . Kano-IPA model . Product improvement . Theme park
* Jiali Kong [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Since the successful development of American Disney in 1955, theme parks have developed rapidly in the world. However, a series of problems caused by over-investment have become increasingly prominent, such as the serious homogenization of tourism products, the serious insufficiency of theme characteristics, the mismatch between investment scale and regional tourist market, excessive dependence on ticket revenue, and insufficient innovation of derivative products. At present, 70% of China’s theme parks are in a state of loss, 20% are in a flat state, and only 10% are in a profitable state [22]. As a service-oriented enterprise, the business philosophy of the theme park is centered on customer demands, and its focus is on improving customer satisfaction by p
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