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New Art of the Problem video on Recommender Systems
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#recommendersystems#collaborativefiltering#matrixfactorization
The Math Behind Recommender Systems
Art of the Problem
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- Recommended by 1 person: 1 in engineering communities.
- May be out of date. Uploaded 6 years ago, in an area where tools change fast.
Beginner. No background in the topic needed.
318K views6 years ago1 recommendation
AI and machine learningComputer scienceClassic machine learningAlgorithms
An explanation of the mathematics behind recommender systems, covering content filtering, collaborative filtering, and matrix factorization.
How do Netflix, YouTube, and other platforms predict what you'll watch next? Dive into the fascinating world of recommender systems (Content Filtering vs Collaborative Filtering) and the mathematics behind your personalized recommendations. In this video, we explore: - Content Filtering vs Collaborative Filtering - The Netflix Prize Problem - Matrix Factorization explained simply - How patterns in user behavior predict preferences - Real-world applications beyond movies Perfect for students and professionals interested in: - Machine Learning - Data Science - AI Systems - Software Engineering - Recommendation Algorithms Original Research Paper: "Matrix Factorization Techniques for Recommender Systems" inf.unibz.it/~ricci/ISR/papers/ieeecomputer.pdf Key Timestamps: The Netflix Prize Problem Content Filtering Explained Collaborative Filtering Approach Matrix Factorization #MachineLearning Paper in this video: Matrix Factorization Techniques for Recommender Systems inf.unibz.it/~ricci/ISR/papers/ieeecomputer.pdfWhy it's here
1 person recommends thisWhat people say. Recommended as an accessible introduction to the mathematics and algorithms behind recommendation engines like Netflix's.