Existing recommendation systems
Modern recommendation systems uses Machine Learning (ML) techniques to make better predictions about user’s preferences, based on the available data that can come from:
- User behavior datainsights about user interaction with a product. This data can be acquired from factors like user ratings, clicks, and purchase records.
- User demographic data refers to personal information about users, including details like age, educational background, income level, and geographical location.
- Product attribute data involves information about the characteristics of a product, such as the genre for books, cast for movies, or cuisine for food."
As of today, some of the most popular ML rechniques are K-nearest neighbors, dimensionality reduction and Neural networks.
K-Nearest Neighbors
K-nearest neighbor (KNN) is a machine learning algorithm that can be used for both classification and regression problems. It works by finding the k closest data points to a new...
