Deep learning recommendation uses neural networks to model user-item interactions, sequential behavior, graph relationships, or multimodal item features.
Approaches
Neural collaborative filtering replaces classical matrix factorization with neural networks that learn non-linear user-item interactions.
Autoencoders learn compressed representations of user preferences and reconstruct user-item interactions.
Recurrent neural networks model sequential behavior and can predict the next item in a sequence.
Graph neural networks model relationships between users, items, and other entities.
Advantages
- captures complex non-linear patterns
- handles diverse features such as images, text, and sequences
- can reach strong performance with enough data
Disadvantages
- data hungry
- computationally expensive
- harder to interpret
- can overfit with small datasets
Deep learning recommenders connect to Deep Learning because they inherit the same optimization, regularization, and representation-learning concerns.