Providing personalized recommendations
If you run an e-commerce site, a feature that is nice to have is recommendations. Using the Recommendation API, you can easily add this. Utilizing Microsoft Azure Machine Learning, the API can be trained to recognize items that should be recommended.
There are three common scenarios for recommendations:
- Frequently Bought Together (FBT): FBT is the scenario where items that are often bought together with other items are recommended. An example of this is if you buy a mouse; the API will then recommend a keyboard.
- Item to Item Recommendations (I2I): I2I is the scenario where certain items are often viewed after other items. Typically, this will be in the form of people who visited this item also visited this other item.
- Customer to Item Recommendations (U2I): U2I is the scenario where you utilize a customer's previous actions to recommend items. If you sell movies, you can recommend other movies based on a customer's previous movie choices.
The general steps...
