Fog Computing-Based Smart Consumer Recommender Systems
The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the clou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/19/1/32 |
| _version_ | 1797240357366267904 |
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| author | Jacob Hornik Chezy Ofir Matti Rachamim Sergei Graguer |
| author_facet | Jacob Hornik Chezy Ofir Matti Rachamim Sergei Graguer |
| author_sort | Jacob Hornik |
| collection | DOAJ |
| description | The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper. |
| first_indexed | 2024-04-24T18:06:09Z |
| format | Article |
| id | doaj.art-977ff91a5b98466d9833a07cbef9b400 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-04-24T18:06:09Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-977ff91a5b98466d9833a07cbef9b4002024-03-27T13:50:22ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762024-03-0119159761410.3390/jtaer19010032Fog Computing-Based Smart Consumer Recommender SystemsJacob Hornik0Chezy Ofir1Matti Rachamim2Sergei Graguer3Coller School of Management, Tel-Aviv University, Tel-Aviv 6997801, IsraelSchool of Business Administration, The Hebrew University of Jerusalem, Jerusalem 9190501, IsraelGraduate School of Business Administration, Bar-Ilan University, Ramat-Gan 5290002, IsraelDepartment of Economics and Management, The Faculty of Economics, Ashkelon Academic College, Ashkelon 78211, IsraelThe latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.https://www.mdpi.com/0718-1876/19/1/32fog computingrecommender systeminternet of things (IoT)edge computingartificial intelligence (AI)software defined networks (SDNs) |
| spellingShingle | Jacob Hornik Chezy Ofir Matti Rachamim Sergei Graguer Fog Computing-Based Smart Consumer Recommender Systems Journal of Theoretical and Applied Electronic Commerce Research fog computing recommender system internet of things (IoT) edge computing artificial intelligence (AI) software defined networks (SDNs) |
| title | Fog Computing-Based Smart Consumer Recommender Systems |
| title_full | Fog Computing-Based Smart Consumer Recommender Systems |
| title_fullStr | Fog Computing-Based Smart Consumer Recommender Systems |
| title_full_unstemmed | Fog Computing-Based Smart Consumer Recommender Systems |
| title_short | Fog Computing-Based Smart Consumer Recommender Systems |
| title_sort | fog computing based smart consumer recommender systems |
| topic | fog computing recommender system internet of things (IoT) edge computing artificial intelligence (AI) software defined networks (SDNs) |
| url | https://www.mdpi.com/0718-1876/19/1/32 |
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