A deep hybrid learning model for customer repurchase behavior

Kim, Jina; Ji, Honggeun; Oh, Soyoung; Hwang, Shu-Yuen; Park, Eunil; Pobil, Ángel P. del

doi:10.1016/j.jretconser.2020.102381

Cited by 25 publications

(15 citation statements)

References 26 publications

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“…We applied the synthetic minority over-sampling technique (SMOTE) for the machine learning classifiers [28]; moreover, we adjusted class weights in the cross-entropy function of the deep neural networks to handle class imbalance (Fig. 2) [29,30].…”

Section: Classification Modelsmentioning

confidence: 99%

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Ji¹,

Oh²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

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In the field of natural language processing (NLP), the advancement of neural machine translation has paved the way for cross-lingual research. Yet, most studies in NLP have evaluated the proposed language models on well-refined datasets. We investigate whether a machine translation approach is suitable for multilingual analysis of unrefined datasets, particularly, chat messages in Twitch. In order to address it, we collected the dataset, which included 7,066,854 and 3,365,569 chat messages from English and Korean streams, respectively. We employed several machine learning classifiers and neural networks with two different types of embedding: word-sequence embedding and the final layer of a pre-trained language model. The results of the employed models indicate that the accuracy difference between English, and English to Korean was relatively high, ranging from 3% to 12%. For Korean data (Korean, and Korean to English), it ranged from 0% to 2%. Therefore, the results imply that translation from a low-resource language (e.g., Korean) into a high-resource language (e.g., English) shows higher performance, in contrast to vice versa. Several implications and limitations of the presented results are also discussed. For instance, we suggest the feasibility of translation from resource-poor languages for using the tools of resource-rich languages in further analysis.

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Section: Classification Modelsmentioning

confidence: 99%

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Ji¹,

Oh²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

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“…This method can be applied to the study of other perception processes in cognition and computer neuroscience ( Hung and Chang, 2021 ). Kim et al (2021) used deep hybrid learning algorithms to analyze customer-oriented data and predicted customer buyback behavior of smartphones of the same brand. The research results show that the model based on the deep hybrid learning algorithm has a prediction accuracy of more than 90%, which provides an effective reference for innovating future marketing strategies ( Kim et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

“… Kim et al (2021) used deep hybrid learning algorithms to analyze customer-oriented data and predicted customer buyback behavior of smartphones of the same brand. The research results show that the model based on the deep hybrid learning algorithm has a prediction accuracy of more than 90%, which provides an effective reference for innovating future marketing strategies ( Kim et al, 2021 ). Luo and Xu (2021) studied the impact of website reviews on the dining decisions of customers during coronavirus disease 2019 (COVID-19) period and combined the deep learning with customer reviews to evaluate the characteristics of the restaurant.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

Zhong

Zhang

et al. 2022

Front. Psychol.

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In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

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“…Precious research has been done by authors [8] who have studied whether a deep hybrid learning approach using various customer data can help predict customer repeat purchases and future behavior. The results were surprising; with a deep hybrid machining approach, predicting up to 90% of consumer behavior concerning repeat purchases is possible.…”

Section: Related Workmentioning

confidence: 99%

E-Commerce Retail Customers Repurchase Factors Influencing Identification

Pekša

2021

ETR

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With the fast-paced technological development era and the importance of using the Internet in our daily lives, e-commerce as shopping no longer seems new or unusual. Regardless of type or size, companies are using e-commerce advantage to compete in the market. Each of these companies needs a circle of independent and loyal customers. When the customer is satisfied, he gives positive feedback about the company and makes a repeat purchase. In this way, he attracts new customers to the company and provides an independent income for the company. Certain factors influence the customer's attitude and behavior. It is essential to determine what influences their customers' choices when competing to earn profit in the market; the customer must be satisfied in order for him to want to make a repurchase. Repurchases from customers indicate a loyalty to the company. Customer loyalty can be the result of a company consistently meeting and exceeding its customer expectations. Customer loyalty can have a significant impact on business growth. To assess and identify the factors influencing customer satisfaction, they are identified and offered a conceptual reflection of the current situation and offer a conceptual model of Identification Causes and Effects of Customer Satisfaction Framework (IceCSF) in e-commerce retail.

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A deep hybrid learning model for customer repurchase behavior

Cited by 25 publications

References 26 publications

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

E-Commerce Retail Customers Repurchase Factors Influencing Identification

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