Difference between revisions of "Recommendation"
m |
m |
||
| (27 intermediate revisions by the same user not shown) | |||
| Line 2: | Line 2: | ||
|title=PRIMO.ai | |title=PRIMO.ai | ||
|titlemode=append | |titlemode=append | ||
| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
| + | <!-- Google tag (gtag.js) --> | ||
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
}} | }} | ||
| − | [ | + | [https://www.youtube.com/results?search_query=~recommender+~recommendation+AI YouTube] |
| − | [ | + | [https://www.quora.com/search?q=~recommender%20recommendation%20AI ... Quora] |
| + | [https://www.google.com/search?q=~recommender+~recommendation+AI ...Google search] | ||
| + | [https://news.google.com/search?q=~recommender+~recommendation+AI ...Google News] | ||
| + | [https://www.bing.com/news/search?q=~recommender+~recommendation+AI&qft=interval%3d%228%22 ...Bing News] | ||
| − | * [[ | + | * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]]. [[...find outliers]] |
| − | + | * [[End-to-End Speech]] ... [[Synthesize Speech]] ... [[Speech Recognition]] ... [[Music]] | |
| − | * [[AI Solver]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] |
| + | * [[Humor]] ... [[Writing/Publishing]] ... [[Storytelling]] ... [[AI Generated Broadcast Content|Broadcast]] ... [[Journalism|Journalism/News]] ... [[Podcasts]] ... [[Books, Radio & Movies - Exploring Possibilities]] | ||
| + | * [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]] ... [[Art]] ... [[Photography]] | ||
| + | * [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] | ||
* [[Case Studies]] | * [[Case Studies]] | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
** [[Fashion]] | ** [[Fashion]] | ||
| + | * [[Reading Material & Glossary|Reading/Glossary]] ... [[Courses & Certifications|Courses/Certs]] ... [[Podcasts]] ... [[Books, Radio & Movies - Exploring Possibilities]] ... [[Help Wanted]] | ||
* [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] | ||
| + | * [http://medium.com/@srnghn/machine-learning-trying-to-make-recommendations-ea2912cf468 Machine Learning: Trying to make recommendations | Stacey Ronaghan - Medium] | ||
| + | |||
Can items can be directly related to users? | Can items can be directly related to users? | ||
| Line 26: | Line 39: | ||
* ... no, have small to medium dataset, then [[Matrix Factorization]] | * ... no, have small to medium dataset, then [[Matrix Factorization]] | ||
| − | + | AI recommendation systems are AI software that recommends products and services to the user of the product or services based on the preferences and choices of the user. The system uses AI to suggest information, products, and services to end users based on analyzed data. This “recommendation” could be derived from a variety of factors, including the user’s digital habits, as well as histories, preferences, interests, and behaviors of similar users. | |
| − | |||
| Line 49: | Line 61: | ||
* [http://www.techemergence.com/business-intelligence-through-intellectual-property-analytics-facebook-amazon/ Business Intelligence Through Intellectual Property Analytics –] Examining [[Meta|Facebook]] and [[Amazon]] | Valuenex | * [http://www.techemergence.com/business-intelligence-through-intellectual-property-analytics-facebook-amazon/ Business Intelligence Through Intellectual Property Analytics –] Examining [[Meta|Facebook]] and [[Amazon]] | Valuenex | ||
* [http://infolab.stanford.edu/~ullman/mmds/ch9.pdf Recommendation Systems | Stanford InfoLab] | * [http://infolab.stanford.edu/~ullman/mmds/ch9.pdf Recommendation Systems | Stanford InfoLab] | ||
| − | * [http://becominghuman.ai/introduction-to-recommendation-system-in-javascript-74209c7ff2f7 Introduction To Recommendation system In | + | * [http://becominghuman.ai/introduction-to-recommendation-system-in-javascript-74209c7ff2f7 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 | Grouping related items together without labeling them, e.g. grouping patient records with similar symptoms without knowing their symptoms | ||
| Line 98: | Line 110: | ||
<youtube>-UqCfc8nmOQ</youtube> | <youtube>-UqCfc8nmOQ</youtube> | ||
| − | === Cortana Analytics: Building a recommendations model === | + | === Cortana [[Analytics]]: Building a recommendations model === |
<youtube>xgU9Wu2z8Ok</youtube> | <youtube>xgU9Wu2z8Ok</youtube> | ||
Latest revision as of 21:41, 5 March 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- End-to-End Speech ... Synthesize Speech ... Speech Recognition ... Music
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Humor ... Writing/Publishing ... Storytelling ... Broadcast ... Journalism/News ... Podcasts ... Books, Radio & Movies - Exploring Possibilities
- Video/Image ... Vision ... Enhancement ... Fake ... Reconstruction ... Colorize ... Occlusions ... Predict image ... Image/Video Transfer Learning ... Art ... Photography
- Prescriptive & Predictive Analytics ... Predictive Operations ... Forecasting ... with Excel ... Market Trading ... Sports Prediction ... Marketing ... Politics
- Case Studies
- Reading/Glossary ... Courses/Certs ... Podcasts ... Books, Radio & Movies - Exploring Possibilities ... Help Wanted
- Collaborative Filtering | Wikipedia
- Personalized Marketing | Wikipedia
- Machine Learning: Trying to make recommendations | Stacey Ronaghan - Medium
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
AI recommendation systems are AI software that recommends products and services to the user of the product or services based on the preferences and choices of the user. The system uses AI to suggest information, products, and services to end users based on analyzed data. This “recommendation” could be derived from a variety of factors, including the user’s digital habits, as well as histories, preferences, interests, and behaviors of similar users.
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
