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.
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.
