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== Building Recommendation Systems in Microsoft Azure == | == Building Recommendation Systems in Microsoft Azure == | ||
Revision as of 09:59, 13 July 2020
Youtube search... ...Google search
- Capabilities
- Clustering
- AI Solver
- Collaborative Filtering | Wikipedia
- Personalized Marketing | Wikipedia
Can items can be directly related to users?
- ... yes, K-Nearest Neighbors (KNN)
- ... no, have a very large dataset, then Alternating Least Squares (ALS)
- ... no, have small to medium dataset, then Matrix Factorization
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.
systems recommend items based on similarity measures between users and/or items. The items recommended to a user are those preferred by similar users.
Contents
Recommender Systems
- Recommender System | Wikipedia
- Recommendation System Algorithms | Daniil Korbut
- Business Intelligence Through Intellectual Property Analytics – Examining Facebook and Amazon | Valuenex
- Recommendation Systems | Stanford InfoLab
- Introduction To Recommendation system In Javascript | Oni Stephen - Medium
Grouping related items together without labeling them, e.g. grouping patient records with similar symptoms without knowing their symptoms
Building Recommendation Systems in Google Cloud Platform (GCP)
Building Recommendation Systems in Amazon Web Services (AWS)
- [Amazon]]
Building Recommendation Systems in Microsoft Azure
Azure Machine Learning Studio: Matchbox Recommender
Cortana Analytics: Building a recommendations model
