International Journal of Advance Computational Engineering and Networking (IJACEN)
.
Follow Us On :
current issues
Volume-10,Issue-8  ( Aug, 2022 )
Past issues
  1. Volume-10,Issue-7  ( Jul, 2022 )
  2. Volume-10,Issue-6  ( Jun, 2022 )
  3. Volume-10,Issue-5  ( May, 2022 )
  4. Volume-10,Issue-4  ( Apr, 2022 )
  5. Volume-10,Issue-3  ( Mar, 2022 )
  6. Volume-10,Issue-2  ( Feb, 2022 )
  7. Volume-10,Issue-1  ( Jan, 2022 )
  8. Volume-9,Issue-12  ( Dec, 2021 )
  9. Volume-9,Issue-11  ( Nov, 2021 )
  10. Volume-9,Issue-10  ( Oct, 2021 )

Statistics report
Dec. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 116
Paper Published : 1401
No. of Authors : 3560
  Journal Paper


Paper Title :
Graph-Based Embeddings to Optimize Website Segmentation for Digital Ad Campaigns

Author :Vyom Pankajkumar Bhatt, Dushyant Rai Tara

Article Citation :Vyom Pankajkumar Bhatt ,Dushyant Rai Tara , (2020 ) " Graph-Based Embeddings to Optimize Website Segmentation for Digital Ad Campaigns " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 14-21, Volume-8,Issue-11

Abstract : Digital Advertising is a form of advertisement that uses the internet as a medium of reaching out to customers. Advertisers identify websites on the internet that are visited by their potential customers and serve ads by bidding on the ad slots available. Every day, billions of such bids take place in the form of online programmatic auctions where advertisers compete for an ad-slot by bidding for it. The process of identifying where, and to whom an advertiser should serve an ad is referred to as a targeting strategy. Broadly, targeting strategies can fall into 2 buckets: Cookie-based targeting where browser cookies are identified to serve ads to relevant users; Contextual targeting where websites relevant to the advertiser are identified to bid for their ad slots. Due to growing privacy concerns where browsers are taking down cookies and recent regulations like General Data Protection Regulation (GDPR) in Europe, cookie-based targeting has become difficult. With the inevitable deprecation of cookie-based strategies down the line, it becomes paramount to identify sophisticated contextual targeting strategies that can be leveraged by advertisers. This paper proposes a data-driven approach to create a new contextual strategy from web traffic data that segments websites into groups for targeting. Firstly, researched geometric deep learning techniques are used to generate website embeddings i.e. representing the websites in a vector space. These embeddings are clustered into website segments that would be used for optimizing digital advertising.This paper then compares different techniques discussed using a heuristic criterion to identify the most optimal method for vector representation. Keywords - Real-time bidding, Cookie-less Advertising, GDPR, Graph Embeddings, Programmatic Advertising, node2vec, Website Clustering, Neural Network, Knowledge Representation, Semantic Web Techniques.

Type : Research paper

Published : Volume-8,Issue-11


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-17619   View Here

Copyright: © Institute of Research and Journals

| PDF |
Viewed - 50
| Published on 2021-02-22
   
   
IRAJ Other Journals
IJACEN updates
Paper Submission is open now for upcoming Issue.
The Conference World

JOURNAL SUPPORTED BY