Recommendation system for ecommerce using the turicreate library.
Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
- Easy-to-use: Focus on tasks instead of algorithms
- Visual: Built-in, streaming visualizations to explore your data
- Flexible: Supports text, images, audio, video and sensor data
- Fast and Scalable: Work with large datasets on a single machine
- Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps
Data has 45 observations with 5 users and 13 items.
Turi Create provides a
model = turicreate.recommender.create(...)method that will automatically choose an appropriate model for your data set.
In this example, Ranking Factorization Recommender is used to recommend products to users, with RMSE Final Training: 0.849708
Product recommendations for all users, for a new user that does not appear in the data set and for a specific user.
Note: Each product is represented by its product_id
In this case we use java servlets and python flask for the backend together with postgresql, in addition, we use JavaScript + Bootstrap + Css for the frontend of our bike store.
