Difference between revisions of "Recommendation"

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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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[http://www.youtube.com/results?search_query=recommender+systems+Recommendation+engine~Text+~grouping+~clustering+artificial+intelligence+deep+learning Youtube search...]
 
[http://www.youtube.com/results?search_query=recommender+systems+Recommendation+engine~Text+~grouping+~clustering+artificial+intelligence+deep+learning Youtube search...]
 
[http://www.google.com/search?q=recommender+systems+Recommendation+engine~Text+~grouping+~clustering+deep+machine+learning+ML ...Google search]
 
[http://www.google.com/search?q=recommender+systems+Recommendation+engine~Text+~grouping+~clustering+deep+machine+learning+ML ...Google search]

Revision as of 13:38, 2 February 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