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Recommendation Systems in Machine Learning
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*Recommendation Systems in Machine Learning*
*What do recommendation systems do?*
Recommendation systems are a fascinating application of machine learning. They help predict what you might like based on your past behavior and preferences.
What are the main types of recommendation systems?
There are mainly two types of recommendation systems: Collaborative Filtering and Content-Based Filtering. Each has its unique way of recommending items.
Collaborative Filtering makes recommendations based on the preferences of similar users. It’s like asking your friends for movie suggestions because they have similar tastes.
There are two main types of Collaborative Filtering: User-Based and Item-Based. User-based filters look at users similar to you, while Item-Based filters look at items similar to those you’ve liked.
Content-Based Filtering recommends items similar to what you’ve liked before, based on item features. For example, if you’ve watched a lot of action movies, it will suggest more action movies.
*So, do recommendation systems use only one of these methods?*
Not always. There’s also a Hybrid Approach, which combines both Collaborative and Content-Based Filtering. This approach often provides better recommendations by leveraging the strengths of both methods.
A lot happens behind the scenes against each apparently simple-looking recommendation we get online!
These recommendation systems also use different unsupervised and supervised machine learning techniques, including clustering, classification, neural networks, and different approaches for feature selection, to improve their predictions and handle large datasets.
Amazon, Netflix, Spotify, and many other companies use advanced machine learning techniques to provide personalized product recommendations to their users.
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Here is my **Machine Learning/Data Science course playlist**:
Here is my playlist on **Workshops for Data Science and Machine Learning**:
Here is another important playlist -- **Neural Network Fundamentals**:
A playlist on **Modern AI algorithms** here:
--
Dr. Shahriar Hossain
*What do recommendation systems do?*
Recommendation systems are a fascinating application of machine learning. They help predict what you might like based on your past behavior and preferences.
What are the main types of recommendation systems?
There are mainly two types of recommendation systems: Collaborative Filtering and Content-Based Filtering. Each has its unique way of recommending items.
Collaborative Filtering makes recommendations based on the preferences of similar users. It’s like asking your friends for movie suggestions because they have similar tastes.
There are two main types of Collaborative Filtering: User-Based and Item-Based. User-based filters look at users similar to you, while Item-Based filters look at items similar to those you’ve liked.
Content-Based Filtering recommends items similar to what you’ve liked before, based on item features. For example, if you’ve watched a lot of action movies, it will suggest more action movies.
*So, do recommendation systems use only one of these methods?*
Not always. There’s also a Hybrid Approach, which combines both Collaborative and Content-Based Filtering. This approach often provides better recommendations by leveraging the strengths of both methods.
A lot happens behind the scenes against each apparently simple-looking recommendation we get online!
These recommendation systems also use different unsupervised and supervised machine learning techniques, including clustering, classification, neural networks, and different approaches for feature selection, to improve their predictions and handle large datasets.
Amazon, Netflix, Spotify, and many other companies use advanced machine learning techniques to provide personalized product recommendations to their users.
----------
Here is my **Machine Learning/Data Science course playlist**:
Here is my playlist on **Workshops for Data Science and Machine Learning**:
Here is another important playlist -- **Neural Network Fundamentals**:
A playlist on **Modern AI algorithms** here:
--
Dr. Shahriar Hossain