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

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.