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World Congress 2023 • Oct 6, 2023

How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)

Dora Petrella

A data science team replaced their underperforming SQL recommender with a Meta-Prod2Vec neural network. Learn how injecting article metadata solved long-tail recommendations and boosted product coverage by 45 percent.

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#1 about 2 min

Identifying the need for product replacement recommendations

Out-of-stock items drive customers to competitors unless relevant alternatives are immediately surfaceable.

#2 about 2 min

Understanding data sources in a complex b2b environment

Combining internal systems, website events, and purchase histories provides the foundational data for machine learning models.

#3 about 3 min

Defining multiple business use cases for recommendations

A flexible recommendation architecture serves diverse needs ranging from customer-facing web pages to internal order management tools.

#4 about 3 min

Evaluating limitations of legacy sql-based recommenders

Statistical formulas calculated continuously in sql struggle to provide accurate recommendations for newly listed or unpopular items.

#5 about 3 min

Adapting natural language algorithms for product recommendations

Applying prod2vec logic to user interaction sessions allows engineers to derive implicit training labels using sliding context windows.

#6 about 2 min

Training a neural network with one-hot encoded vectors

Feeding product combinations into a hidden layer model generates probability scores for items appearing within the same session.

#7 about 2 min

Improving long-tail predictions using meta-prod2vec algorithms

Concatenating category and brand metadata into product vectors significantly improves alternative matching for infrequently purchased items.

#8 about 1 min

Calculating vector distances to generate final alternatives

Measuring cosine similarity between geometric array representations allows the system to surface the most contextually relevant product replacements.

#9 about 2 min

Building the machine learning engineering pipeline

Connecting dbt transformations with vertex ai and orchestrating regular automated training runs ensures clean data flows into the elasticsearch storage.

#10 about 3 min

Evaluating model performance through offline metrics mapping

Testing against a production-like distribution utilizing ndcg and shadow traffic analysis confirms improvements in latency and catalog coverage.

#11 about 4 min

Visualizing product embeddings to validate clustering logic

Plotting multidimensional vectors in an open-source 3d projector tool confirms that related product attributes cluster accurately.

#12 about 2 min

Establishing pragmatic business baselines for model evaluation

Implementing a simple heuristic baseline metric sets a measurable quality gate and builds trust with non-technical stakeholders.

#13 about 2 min

Automating exploratory data analysis within training pipelines

Embedding automated html plotting directly into the model training pipeline prevents bad data from silently corrupting updated training runs.

#14 about 2 min

Team composition and realistic product launch timelines

Delivering an end-to-end recommendation system requires cross-functional collaboration and often exceeds initial baseline timeline estimates.

Matching moments

1:50 min

Real life recommendation systems and final project conclusions

Lutske van der Meer Lutske van der Meer · WWC 2024

2:40 min

Understanding real-world recommendation systems in common platforms

Julian Joseph · LIVE

2:14 min

Assessing the future of AI in web performance optimization

Perf + AI

4:42 min

Predicting future retail experiences and avoiding technology hype

Alejandro Saucedo Alejandro Saucedo +2 · WWC 2025

57 sec

Implementing predictive prefetch with machine learning

Jessica Janiuk · JS Congress

2:50 min

Driving subtle product enhancements with historical platform data

Alejandro Saucedo Alejandro Saucedo +2 · WWC 2025

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