DL Models
Created: 2022-03-23 12:53
Pros:
- they can deal with non linear transformations;
- good for sequence modeling, next basket recommendation or session based recommendations (for example #click-logs);
- representation learning Autoencoder Restricted Boltzmann machine(RBM);
- easier to build hybrid systems
Cons:
- interpretability -> black boxes;
- they need a big amount of data;
- hyperparameter tuning can take longer time;
Types:
- Multilayer Perceptron (MLP) -> feed-forward neural network with multiple hidden layers, i.e. stacked layers of nonlinear transformations;
- CNN can capture global and local features, good for grid-like data;
- RNN for sequential data;
- Autoencoder are unsupervised models that use bottleneck layer to learn salient features;
- *Restricted Boltzmann machine(RBM)*are two layers models (visible and hidden layer);
- Neural attentionthat use attention mechanism;
- Neural Autoregressive Distribution Estimation (NADE) are unsupervised neural networks built on top of a autoregressive model and feedforward neural networks;
- Adversarial Networks (AN) consist of a discriminator and a generator which compete against each other;
- Deep Reinforcement Learninig (DRL) operate on a trial-and-error paradigm;
- Transformer for sequential and session-based recommendation
Obviously a DL-based RecSys can use just one DL model or a combination of several DL models ( #deep_hybrid_models ).
Some combinations of models that have been proved to be effective are:
- CNN + Autoencoder;
- CNN + RNN;
- RNN + Autoencoder;
- Rnn + DRL
References
- https://arxiv.org/pdf/1707.07435.pdf
- https://medium.com/sciforce/deep-learning-based-recommender-systems-b61a5ddd5456
- https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9354169&tag=1
Code
Tags
#dl #rnn #cnn #mlp #rbm #drl #nade #autoencoder #an #neural_attention #transformer