From the course: Building Recommender Systems with Machine Learning and AI
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Bleeding edge alert: Deep factorization machines - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Bleeding edge alert: Deep factorization machines
- It's time for another bleeding edge alert. This is where we talk about some recent research that has promising results but hasn't made it into the mainstream with recommender systems yet. If you remember back to the matrix factorization section of this course, we mentioned factorization machines toward the end of it. They are a more general form of matrix factorization and can be designed to do the same thing as SVD as an example. But they take in categorical data, and can find latent relationships between any combination of the features they are given. As such, factorization machines are more general purpose than SVD and can sometimes find relationships between features that SVD wouldn't have considered. The idea of deep factorization machines is to combine the power of factorization machines with the power of deep neural networks to create an even more powerful recommender system. This hybrid approach outperforms factorization machines or neural networks used individually, albeit,…
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Contents
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Intro to deep learning for recommenders2m 19s
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Restricted Boltzmann machines (RBMs)8m 2s
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Recommendations with RBMs, part 112m 46s
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Recommendations with RBMs, part 27m 11s
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Evaluating the RBM recommender3m 44s
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Tuning restricted Boltzmann machines1m 43s
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Exercise results: Tuning a RBM recommender1m 15s
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Auto-encoders for recommendations: Deep learning for recs4m 27s
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Recommendations with deep neural networks7m 23s
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Clickstream recommendations with RNNs7m 23s
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Get GRU4Rec working on your desktop2m 42s
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Exercise results: GRU4Rec in action7m 51s
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Bleeding edge alert: Deep factorization machines5m 49s
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More emerging tech to watch5m 14s
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