Difference between revisions of "Recommendation"
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* [[Clustering]] | * [[Clustering]] | ||
* [[AI Solver]] | * [[AI Solver]] | ||
| + | * [[Television (TV)]] | ||
* [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] | ||
Revision as of 19:52, 16 June 2021
Youtube search... ...Google search
- Capabilities
- Clustering
- AI Solver
- Television (TV)
- 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
Google Cloud Platform (GCP)
Amazon Web Services (AWS)
Microsoft Azure
Azure Machine Learning Studio: Matchbox Recommender
Cortana Analytics: Building a recommendations model
