CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Advertisement and Document Recommendation based on Content in the Image

by Meenakshi Chandak, A. S. Ghotkar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 20
Year of Publication: 2018
Authors: Meenakshi Chandak, A. S. Ghotkar
10.5120/ijca2018917968

Meenakshi Chandak, A. S. Ghotkar . Advertisement and Document Recommendation based on Content in the Image. International Journal of Computer Applications. 182, 20 ( Oct 2018), 12-16. DOI=10.5120/ijca2018917968

@article{ 10.5120/ijca2018917968,
author = { Meenakshi Chandak, A. S. Ghotkar },
title = { Advertisement and Document Recommendation based on Content in the Image },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 20 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number20/30048-2018917968/ },
doi = { 10.5120/ijca2018917968 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:56.490479+05:30
%A Meenakshi Chandak
%A A. S. Ghotkar
%T Advertisement and Document Recommendation based on Content in the Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 20
%P 12-16
%D 2018
%I Foundation of Computer Science (FCS), NY, 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.

References
  1. T. Mei, X. Hua, “Contextual In-Image Advertising,” Proceedings of the 16th ACM International Conference on Multimedia, pp. 439-448, 2008.
  2. W. Jiang, Dechao Liu, “An Online Advertisement Platform based on Image Content Bidding,” IEEE International Conference on Multimedia and Expo, pp. 1234 - 1237, 2009.
  3. Y. Chen, O. Jin, “Visual Contextual Advertising: Bringing Textual Advertisements to Images,” Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp. 1314-1320, 2010.
  4. D. V. Phuong, T. M. Phuong, “A Keyword-Topic Model for Contextual Advertising,” Proceedings of the ThirdSymposium ACM on Information and Communication Technology, pp. 63-70, 2012.
  5. Y. Kalantidis, A. Farahat, “Visual Congruent Ads for Image Search,” Proceedings of 23rd International Conference on Pattern Recognition in IEEE, pp. 1496-1505, 2016.
  6. A. Broder, M. Fontoura, “A Semantic Approach to Contextual Advertising,” Proceedings of the 30th annual international ACM SIGIR Conference on Research and development in Information Retrieval, pp. 559-566, 2007.
  7. C. Xiang, T. V. Nguyen, “SalAd: A Multimodal Approach for Contextual Video Advertising,” Proceeding in IEEE International Symposium on Multimedia, pp. 211-216, 2015.
  8. S. Wang, Z. Chen, “Identifying Search Keywords for Finding Relevant Social Media Posts,” AAAI Conference on Artificial Intelligence, pp. 3052-3058, 2016.
  9. W. Zhang, D. Wang, “Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia,” ACM Transactions on Intelligent Systems and Technology archive, vol. 3, no. 2, pp. 3101-3136, 2012.
  10. Y. Y. Chen, T. Chen, “Predicting Viewer Affective Comments Based on Image Content in Social Media,” Proceedings in ACM International Conference on Multimedia Retrieval, pp. 233-241, 2014.
  11. T. Chen, F. X. Yu, “Object-Based Visual Sentiment Concept Analysis and Application,” Proceedings of the 22nd ACM International Conference on Multimedia, pp. 367-376, 2014.
  12. T. Mei, X. S. Hua, “Contextual Internet Multimedia Advertising,” Proceedings of the IEEE, vol. 98, no. 8, pp. 1416 - 1433, Apr 2010.
  13. J. Sumalatha, H. Girish, “Topic Modeling using TF-IDF and Linked Data,” International Journal of Engineering Research in Computer Science and Engineering, vol. 5, no. 4, pp. 2320-2394, 2018.
  14. Y. Feng and M. Lapata, “Topic Models for Image Annotation and Text Illustration,” Proceeding Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 831-839, 2010.
  15. M. Abadi, M. Isard, “A computational model for TensorFlow: an introduction,” Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, pp. 1-7, 2017.
  16. J. Huang, C. Sun, “Speed/accuracy trade-offs for modern convolutional object detectors,” Proceedings of Cornell University Library in Computer Vision and Pattern Recognition, pp. 7310-7319, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Advertisement Recommendation Document Retrieval Object Detection Topic Modeling.

Powered by PhDFocusTM