Recommendation Systems/Ch. 2 — Neural Network Based Recommenders/1 of 6

Why Deep Learning?

Traditional collaborative filtering methods hit a ceiling. They cannot automatically extract useful features from raw unstructured data (images, text, audio), they model user-item interactions linearly, and they struggle to scale to production datasets of hundreds of millions of users and billions of items.

Deep learning addresses each of these shortcomings:

Automatic feature extraction: CNNs can extract visual features from product images, transformers can encode item descriptions, no manual feature engineering required
Handling sparse data: Embedding layers learn dense representations even from sparse interaction matrices
Scalability: GPU-accelerated mini-batch training scales to massive datasets
Non-linear and complex interaction modeling: Multi-layer networks can capture higher-order user-item interactions that dot-product similarity misses

Motivation for CNNs — CNNs can be used to extract features from unstructed data that can be used to make better recommendations.

Motivation for Sequence Models — When time matters (i.e. behavior changes after getting a significant other), sequence models can capture these temporal dynamics.

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Ch. 1 — Introduction to Recommenders

Review of Traditional Approaches: Nearest Neighbors

Probabilistic Matrix Factorization

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