ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore consumer behavior across the interconnected metaverse. In this research, previous findings were cumulated showing that real-time predictive analytics, simulation modeling tools, and computer vision algorithms shape immersive retail experiences across customer journey, and we contribute to the literature by indicating that virtual content optimization as regards entertaining metaverse events can be attained by deploying retail business analytics, social commerce capabilities, and deep learning-based ambient sound processing in immersive 3D virtual environments. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the metaverse economy” + “image processing computational algorithms,” “sensory data mining techniques,” and “predictive customer analytics.” As research published in 2022 was inspected, only 144 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 32 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: immersive decentralized networking; customer engagement tools; spatial computing technology; predictive customer analytics; interconnected metaverse; cognitive artificial intelligence algorithms

How to cite: Grupac, M., and Lăzăroiu, G. (2022). “Image Processing Computational Algorithms, Sensory Data Mining Techniques, and Predictive Customer Analytics in the Metaverse Economy,” Review of Contemporary Philosophy 21: 205–222. doi: 10.22381/RCP21202213.

Received 22 May 2022 • Received in revised form 23 August 2022
Accepted 26 August 2022 • Available online 30 August 2022

1Faculty of Humanities, Department of Mediamatics and Cultural Heritage, University of Zilina, Zilina, Slovak Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
2The Institute of Smart Big Data Analytics, New York, NY, USA; Spiru Haret University, Bucharest, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it. (corresponding author).

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