Difference between revisions of "Recommendation"

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* [[Clustering]]
 
* [[Clustering]]
 
* [[AI Solver]]
 
* [[AI Solver]]
* [[K-Nearest Neighbors (KNN)]]
 
* [[Alternating Least Squares (ALS)]]
 
* [[Matrix Factorization]]
 
 
* [http://en.wikipedia.org/wiki/Recommender_system Recommender System | Wikipedia]
 
* [http://en.wikipedia.org/wiki/Recommender_system Recommender System | Wikipedia]
 
* [http://en.wikipedia.org/wiki/Collaborative_filtering Collaborative Filtering | Wikipedia]
 
* [http://en.wikipedia.org/wiki/Collaborative_filtering Collaborative Filtering | Wikipedia]
 
* [http://en.wikipedia.org/wiki/Personalized_marketing Personalized Marketing | Wikipedia]
 
* [http://en.wikipedia.org/wiki/Personalized_marketing 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]]
  
  

Revision as of 23:05, 6 January 2019

Youtube search... ...Google search

Can items can be directly related to 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

Building Recommendation Systems in Azure

Azure Machine Learning Studio: Matchbox Recommender

Cortana Analytics: Building a recommendations model