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Executive Summary

In this article, Inuvo Senior Data Engineer & Data Scientist Dr. Melodie Du explores enhancing the understanding of webpage content at scale using machine learning through vectorization. This topic is increasingly relevant as most web-based advertising impressions move away from persistent identifier-based targeting. In a new cookie-free world, understanding consumer intent in privacy-safe ways is crucial. The main challenge is extracting meaningful concepts from webpages and scoring them consistently across similar pages.

To comprehend consumer intent, Dr. Du employs Natural Language Processing (NLP) techniques, such as breaking down text into N-grams for evaluation and scoring. This process generates up to 300 concepts per webpage. Along high-level categories, demographics, and other statistics, these concepts create a “profile” for each URL. These profiles hold valuable user intent data for discovering and advertising to new audiences. With billions of web pages to index, and each profile containing extensive information, Dr. Du suggests using “feature engineering” to transform raw page profile data into a more manageable form called a “vector” that enhances processing speed and efficiency.

The vectorization process can be compared to compressing jpeg images; the goal is to remove unnecessary signals and reduce the URL profile’s size and complexity without losing resolution. The vectorized profile should retain essential details but be significantly easier for Machine Learning (ML) algorithms to process.

Dr. Du discusses methods for vectorization, including one-hot encoding and experienced selection. However, one-hot encoding has challenges like computational expense and high variance. To address these issues, Dr. Du proposes strategies like experienced selection, term frequency – inverse document frequency (TF-IDF), and hashing to reduce dimensionality while preserving important concepts in the feature engineering phase.


Utilizing suitable feature engineering and vectorization methods can improve the efficiency and accuracy of advertising campaigns. By combining advanced contextual understandings of web pages with generative-AI-powered audience insights, Inuvo enables advertisers to reach their prospective audiences across all devices without the use of persistent or intrusive consumer tracking or data.

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