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Can items can be directly related to users?

AI recommendation systems are AI software that recommends products and services to the user of the product or services based on the preferences and choices of the user. The system uses AI to suggest information, products, and services to end users based on analyzed data. This “recommendation” could be derived from a variety of factors, including the user’s digital habits, as well as histories, preferences, interests, and behaviors of similar users.

  1. Content-based

systems examine attributes of the items recommended. For example, if a Netflix user has watched many cowboy movies, then recommend a movie classified in the datastore as having the “scifi” genre.

  1. Collaborative Filtering (CF)

systems recommend items based on similarity measures between users and/or items. The items recommended to a user are those preferred by similar users.

Recommender Systems

Grouping related items together without labeling them, e.g. grouping patient records with similar symptoms without knowing their symptoms

Google Cloud Platform (GCP)

Amazon Web Services (AWS)

Microsoft Azure

Azure Machine Learning Studio: Matchbox Recommender

Cortana Analytics: Building a recommendations model