LLM‑Powered Graph Models for Ad Relevance

Designing scalable ad relevance with FastText, graph models, BroadGen, and LLM‑distilled tiny BERT in real e‑commerce

What you’ll learn
Design and train scalable keyphrase recommendation systems using FastText, graph‑based models, and BroadGen for extreme multi‑label advertising problems.
Implement and optimize BroadGen string clustering to generate effective broad match keyphrases that balance reach, relevance, and platform efficiency.
Use LLM‑as‑a‑judge signals to construct high‑quality training data, then distill them into tiny BERT or micro‑BERT relevance models for production PLA systems.
Evaluate models with business‑aligned metrics like AVP, relevant reach, search pass rate, CTR, CVR, and ROAS, and interpret trade‑offs for deployment decisions.

Requirements
Comfortable with Python and basic machine learning (training, validation, evaluation, and working with common libraries like scikit‑learn or PyTorch/TensorFlow).
Familiarity with NLP fundamentals such as tokenization, word embeddings, and text classification; prior exposure to FastText or transformer models is helpful but not mandatory.
Basic understanding of information retrieval or recommender systems concepts (queries, items, clicks, impressions) and standard metrics like precision, recall, and CTR.

Description
This course primarily teaches how to design and deploy scalable keyphrase recommendation and ad relevance systems using Fast Text, bipartite graph models, Broad Gen for broad match, and LLM‑distilled tiny BERT relevance models in real e‑commerce advertising pipelines. This course shows how to build real ad relevance systems using a stack of modern models instead of isolated toy examples. Learners start with Fast Text as a strong, CPU‑friendly baseline for extreme multi‑label keyphrase recommendation, then move to bipartite graph models that scale to millions of labels while remaining interpretable and efficient. The course then introduces Broad Gen, a graph‑and‑clustering framework for generating high‑quality broad‑match keyphrases from historical queries, designed to handle shifting query distributions without retraining deep networks. Finally, the course covers how to use LLM‑as‑a‑judge signals to create high‑quality relevance labels and distill them into tiny BERT or micro‑BERT cross‑encoders that can be deployed in production PLA pipelines on CPUs with tight latency budgets. Throughout, you will connect offline metrics to real business outcomes like clicks, conversion, ROAS, and seller sentiment, and see how these models fit together into a coherent, production‑ready architecture for large‑scale e‑commerce advertising. A practical guide to building and scaling keyphrase recommendation and ad relevance systems using FastText baselines, graph‑based models, BroadGen, and LLM‑distilled tiny BERT in real‑world e‑commerce advertising.

Who this course is for
This course is for data scientists, machine learning engineers, and applied researchers who want to design and deploy large‑scale keyphrase and ad relevance systems in real‑world e‑commerce or advertising platforms
It’s ideal for practitioners already familiar with basic NLP and ML who want to go beyond toy examples into extreme multi‑label learning, graph‑based models, and production‑grade evaluation.
It will also benefit ML architects and technical product managers responsible for sponsored search, promoted listings, or recommendation products who need a deep but practical understanding of how FastText, graph models, BroadGen, and LLM‑distilled tiny BERT models fit together in a modern ads stack.

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