What is Recommendations AI?
Recommendations AI generates state-of-the-art, personalised product recommendations for customers. By applying Google’s unparalleled research in machine learning, rich expertise from Google Search and YouTube, and the scalability of GCP, it seeks to understand individual customers and lift key business metrics.
The engine is currently in public beta, with existing customers including Disney, Sephora, Hanes. Four key benefits of Recommendations AI are:
1 – Leading Machine Learning Technology
Each customer has a unique journey through a website. Recommendations AI unifies all the information available, discovers user behaviours and preferences, then forms a unique, individualised prediction for every visit.
Google describes the engine as “context-hungry”: always looking for more user events or product data to form inferences and improve prediction quality.
Recommendations AI brings significant flexibility and customisation. Three unique model types, ‘Recommended for You’, ‘Others You May Like’, and ‘Frequently Bought Together’, are suitable for different pages throughout a store. Models can also be tuned to maximise particular business objectives or apply custom filtering rules.
2 – Scalable Deployments
Recommendation engines must be able to swiftly deliver personalised predictions without increasing website loading times. Retailers know the strain of critical periods, such as seasonal sales and Christmas, with frequent surges of 3 to 10 times normal traffic.
To solve this, Recommendations AI is backed by the scale of the Google Cloud and automatically provisions itself resources on-demand. Its infrastructure is fully-managed, globally deployed, and often delivers latencies of less than 100ms.
3 – Automatic Re-Training
Initial models are only a starting point. Recommendations AI constantly tunes and re-trains models, learning from its successes and incorporating the latest products in recommendations.
4 – Detailed Analytics
Once deployed, the Recommendations AI console unifies single-click maintenance tasks and comprehensive performance analytics. The dashboard details key metrics for each model and placement, allowing for tracking and comparison.
Through a system of recommendation tokens, the AI quantifies revenue gains by identifying which sales lead from engagement with the engine.