#everydayai Everyday Artificial Intelligence Amazon Search and Recommendations Systems

An example knowledge graph on movies and related entities. Finding important nodes in knowledge graphs is important for Amazon, with direct applications. Image: Amazon

Your Amazon searches (“ironing board”, “pizza stone”, “Android charger”, etc.) quickly return a list of the most relevant products related to your search. Amazon doesn’t reveal exactly how its doing this, but in a description of its product search technology,Amazon notes that its algorithms “automatically learn to combine multiple relevance features. Our catalog’s structured data provides us with many such relevance features and we learn from past search patterns and adapt to what is important to our customers.”

You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and “customers who bought this item also bought”, as well as via personalized recommendations on the home page, bottom of item pages, and through email. Amazon uses artificial neural networks to generate these product recommendations.

While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale).

To learn more, check out the following links:

Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs (click here)

Third-Party Recommendation Systems Industry: Current Trends and Future Directions by Amit Sharma :: SSRN (click here)

Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE | AWS Big Data Blog (click here)

Source: Emerj (Click here)

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