Difference between revisions of "Recommendation"

From
Jump to: navigation, search
(Building Recommendation Systems in Amazon Web Services (AWS))
(Recommender Systems)
Line 56: Line 56:
 
<youtube>XoTwndOgXBM</youtube>
 
<youtube>XoTwndOgXBM</youtube>
  
== Building Recommendation Systems in Google Cloud Platform (GCP)==
+
== Google Cloud Platform (GCP)==
 
* [[Google]]  
 
* [[Google]]  
  
Line 64: Line 64:
 
<youtube>807uHC0Ia10</youtube>
 
<youtube>807uHC0Ia10</youtube>
  
== Building Recommendation Systems in Amazon Web Services (AWS) ==
+
== Amazon Web Services (AWS) ==
 
* [[Amazon]]  
 
* [[Amazon]]  
  
Line 72: Line 72:
 
<youtube>m0Pty9v9A_0</youtube>
 
<youtube>m0Pty9v9A_0</youtube>
  
== Building Recommendation Systems in Microsoft Azure ==
+
== Microsoft Azure ==
 
* [[Microsoft]]  
 
* [[Microsoft]]  
  

Revision as of 10:00, 13 July 2020

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

Google Cloud Platform (GCP)

Amazon Web Services (AWS)

Microsoft Azure

Azure Machine Learning Studio: Matchbox Recommender

Cortana Analytics: Building a recommendations model