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Advertisement and Document Recommendation based on Content in the Image

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Meenakshi Chandak, A. S. Ghotkar
10.5120/ijca2018917968

Meenakshi Chandak and A S Ghotkar. Advertisement and Document Recommendation based on Content in the Image. International Journal of Computer Applications 182(20):12-16, October 2018. BibTeX

@article{10.5120/ijca2018917968,
	author = {Meenakshi Chandak and A. S. Ghotkar},
	title = {Advertisement and Document Recommendation based on Content in the Image},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2018},
	volume = {182},
	number = {20},
	month = {Oct},
	year = {2018},
	issn = {0975-8887},
	pages = {12-16},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume182/number20/30048-2018917968},
	doi = {10.5120/ijca2018917968},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In recent years, more and more images have been uploaded and published on the web. Along with text web pages, images have become an important media for various social media platforms to place relevant advertisements. However, conventional image advertising primarily uses text content rather than image content to match relevant advertisements. There is no existing system to automatically monetize the opportunities brought by individual image. As a result, the advertisements are only generally relevant to the entire web page rather than specific to images it contained. To overcome this, advertisements in the proposed system are recommended based on images. The objects are detected from the image using TensorFlow API Model and based on those objects (keywords) advertisements are recommended. An additional application is provided, were based on the detected objects (keywords) relevant documents are recommended using Term Frequency-Inverse Document Frequency algorithm. From the experimental results, it is seen that system could recognize over 90 percent of objects and could recommend relevant advertisement with mean average precision of 0.66.

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Keywords

Advertisement Recommendation, Document Retrieval, Object Detection, Topic Modeling.