Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment

Zheng, Qiang; Ding, Qing Shan

doi:10.1371/journal.pone.0268007

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…This study combines machine learning algorithms with the R-CNN algorithm, using augmented reality to identify products [23]. Zheng et al, designed a study for an immersive marketing environment using the Graph Neural Network (GNN) model to enhance the user's shopping experience and promote sales [30]. Dobre et al, research introduces elements of data processing related to immersive machine learning with the Long-Short Term Memory (LSTM)-Recurrent Neural Network method to detect social engagement and shows how creatives can use virtual reality to expand their capabilities to design more engaging experiences [31].…”

Section: B Discussionmentioning

confidence: 99%

Systematic Literature Review of Machine Learning in Virtual Reality and Augmented Reality

Sindu

Hartati

Sudarma

et al. 2023

j. nas. pendidik. teknik. inform.

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This study aims to conduct a systematic literature review on machine learning methods used in virtual reality (VR) and augmented reality (AR). The method proposed in the SLR consists of three main phases: planning, conducting, and reporting. Researchers used data obtained through Google searches sourced from IEEE Xplore, Springer, Science Direct, Scopus, and Web of Science. The application of inclusion and exclusion criteria is used to select documents. The findings from this study consist of countries involved in machine learning research in the VR and AR fields, machine learning methods, technology used, and work sectors that use machine learning in the VR and AR fields. The results also show that when the machine learning method is used in VR and AR applications, the main advantages are high efficiency and algorithm precision. Moreover, this research observes that machine learning is the most widely applied artificial intelligence scientific technique in VR and AR applications. The results of this systematic literature review show that the combination of machine learning and VR and AR methods contributes to trends, opportunities, and applications in the fields of engineering, arts, education, industry, medicine, tourism, and technology

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Section: B Discussionmentioning

confidence: 99%

Systematic Literature Review of Machine Learning in Virtual Reality and Augmented Reality

Sindu

Hartati

Sudarma

et al. 2023

j. nas. pendidik. teknik. inform.

View full text Add to dashboard Cite

show abstract

“…Zheng and Ding [ 37 ] proposed a personalized recommendation system based on an Immersive Graph Neural Network (IGNN), which is intended to increase the marketing quantity of various commodities, to improve users’ shopping experience, promote sales, and thus motivate the market development. The authors considered an immersive marketing environment using deep learning and graph neural network models.…”

Section: Related Workmentioning

confidence: 99%

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

et al. 2022

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Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their individual purchase histories, weekly specials offered in local stores, and product cost and availability information. We perform clustering analysis to partition given customer profiles into four non-overlapping clusters according to their grocery shopping habits. Then, we conduct computational experiments to compare several traditional ML algorithms and our new DL algorithm based on the use of a gated recurrent unit (GRU)-based recurrent neural network (RNN) architecture. Our DL algorithm can be viewed as an extension of DREAM (Dynamic REcurrent bAsket Model) adapted to multi-class (i.e. multi-store) classification, since a given user can purchase recommended products in different grocery stores in which these products are available. Among traditional ML algorithms, the highest average F-score of 0.516 for the considered data set of 831 customers was obtained using Random Forest, whereas our proposed DL algorithm yielded the average F-score of 0.559 for this data set. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional ML or DL model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art DL models.

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“…The marketplace has changed rapidly over the past few years and is dramatically different from even ten years ago. Dramatic shifts are happening with respect to new marketing behaviors, opportunities, and emerging challenges (Zheng & Ding, 2022). Companies should understand and appreciate the new marketing realities and should develop strategies and initiatives accordingly to face the competition.…”

Section: Introductionmentioning

confidence: 99%

The New Marketing Realities and the Major Marketing Forces

Mandal

2023

Journal of Business Ecosystems

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Companies and businesses operate in a competitive environment. Market forces are shaping new marketing realities for companies. The objective of the study is to conduct an analysis of the various aspects of the market forces viz. technology, globalization, physical environment, and social responsibility, and how these forces are shaping the new marketing realities. The methodology adopted is a conceptual analysis of the various aspects of the market forces and the new marketing realities. Companies adopt initiatives and strategies to convert the challenges into opportunities. Academicians may analyze and suggest effective ways to understand the market forces and the new marketing realities. Companies and practicing managers may analyze how the market forces are shaping the marketing realities and the marketing landscape. They should adopt strategies and initiatives which help them to achieve business excellence in the long run.

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Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment

Cited by 5 publications

References 34 publications

Systematic Literature Review of Machine Learning in Virtual Reality and Augmented Reality

Systematic Literature Review of Machine Learning in Virtual Reality and Augmented Reality

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

The New Marketing Realities and the Major Marketing Forces

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