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IMPROVING FASHION INDUSTRY USING ARTIFICIAL INTELLIGENCE-ENABLED IN-VIDEO ADVERTISEMENTS

Akshay Shah & Siddhesh Nasnodkar

EPH-International Journal of Business & Management Science2023https://doi.org/10.53555/eijbms.v9i3.146article
ABDC C
Weight
0.50

What the paper says

It is crucial for advertisers to comprehend the video context when directing video adverts at consumers, given the growing audience for online video content. Important elements like ad relevancy to video content, where and how video advertisements are displayed, and non-intrusive user experience are required to be looked at in sequence to enhance the consumer experience and quality of commercials. We suggest a methodology for better ad suggestion that meets these requirements by understanding the video content semantically. The study focuses on how machine learning technology has significantly influenced the usage of in-video advertisements as a part of the content advertising approach for fashion brands. Every company wants to get in front of consumers when they are most receptive to persuasion. However, because there are so many options for customers, digital media are generating distinctive customer journeys that take an independent tack. Therefore, there is a need for more compelling advertising strategies to grab consumers' attention and make it possible for them to find companies at peak demand periods.

3 citations

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https://doi.org/https://doi.org/10.53555/eijbms.v9i3.146

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@article{akshay2023,
  title        = {{IMPROVING FASHION INDUSTRY USING ARTIFICIAL INTELLIGENCE-ENABLED IN-VIDEO ADVERTISEMENTS}},
  author       = {Akshay Shah & Siddhesh Nasnodkar},
  journal      = {EPH-International Journal of Business & Management Science},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.53555/eijbms.v9i3.146},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.38 × 0.4 = 0.15
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.