Recommendation API

Confused between products? What to choose, what suits you, what will be the best for you? No problem. Recommendation API helps you solve your confusion about the selection of a product at a catalog. It is powered by Azure Machine Learning. When you visit a digital store (mobile app or Web app) you get the recommendation on basis of your activities.

Recommendation engine collects data directly from the digital store or past customer activity. When a customer visits the digital store again, the customer gets recommendations from a catalog which increase conversion rate.

Recommendation Scenario

Item Recommendation

The most common which use this capability is - ‘customer who clicked/visited on the item also visits/clicks on this item.’

Example, people who visited Harry Potter Series also see Sherlock Holmes Series.

Customer to Item Recommendation

By customer activity, you can recommend items that customer may buy.

Example, the customer who watched horror movie may be interested in more horror movies.

Frequently bought together (FBT) Recommendation

In this scenario, the recommendations engines recommendation products bought together. If you buy one product then the customer who bought same product with one more product, so you get the recommendation for the second product.Example, People who purchase mouse and keyboard together helps recommendation engine to recommend mouse to people who bought keyword and people who bought keyword gets the recommendation for a mouse.

Example, People who purchase mouse and keyboard together helps recommendation engine to recommend mouse to people who bought keyword and people who bought keyword gets the recommendation for a mouse.

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