Machine learning: Difference between revisions
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* Item similarity | * Item similarity | ||
* Context | * Context | ||
== Approaches to recommendation == | |||
* Collaborative filtering: recommend on past behavior | |||
** User based: recommend what other users like who are like me | |||
** Item based: find similar items to those I have previously liked | |||
* Content-based: Recommended based on item features | |||
* Personalized learning to rank: treat recommendations as a tranking problem | |||
* Demographic: recommend based on user features | |||
* Social-recommendations: trust-based | |||
* Hybrid: any combination of the above | |||
== Terminology == | == Terminology == |
Revision as of 09:47, 26 December 2022
Introduction[edit]
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[2]
The recommender problem[edit]
Estimate a utility function that automatically predicts how a user will like an item[3]
Based on:
- Past behavior
- Relation to other users
- Item similarity
- Context
Approaches to recommendation[edit]
- Collaborative filtering: recommend on past behavior
- User based: recommend what other users like who are like me
- Item based: find similar items to those I have previously liked
- Content-based: Recommended based on item features
- Personalized learning to rank: treat recommendations as a tranking problem
- Demographic: recommend based on user features
- Social-recommendations: trust-based
- Hybrid: any combination of the above
Terminology[edit]
- Behavioural data
- Cloud delivery models:
- Cloud deployment models
- Collaborative filtering
- Content-based filtering
- Cost function
- F-measure
- Hyperparameter
- K-nearest neighbour (k-NN) algorithm
- Matrix factorization
- Mean absolute error (MAE)
- Overfitting
- Popularity bias
- Precision
- Recall
- Reinforcement learning
- Right to anonymity
- Right to privacy
- Root-mean-square error (RMSE)
- Stochastic gradient descent
- Training data
Examples[edit]
An excellent, and I truly mean excellent example is MarI/O, a machine learning program that learns how to play mario, and mario kart.
- Super Mario World: https://www.youtube.com/watch?v=qv6UVOQ0F44
- Mario Kart: https://www.youtube.com/watch?v=S9Y_I9vY8Qw