Neural Collaborative Filtering (NCF) is a transformative technique that has revolutionized recommendation systems. This blog offers you a user-friendly guide on implementing NCF based on the influential paper by Xiangnan He et al. In this guide, we’ll explain how to set it up, run examples, and troubleshoot any issues you might encounter along the way!

What is Neural Collaborative Filtering?

NCF is a framework that combines traditional collaborative filtering approaches with neural network architectures to improve the accuracy of recommendations. It integrates three robust models:

• Generalized Matrix Factorization (GMF)
• Multi-Layer Perceptron (MLP)
• Neural Matrix Factorization (NeuMF)

Each model uniquely processes implicit feedback and ranking tasks, optimizing the recommendations further using log loss with negative sampling.

Setting Up Your Environment

To begin with NCF, you need to install the required libraries:

• Keras version: 1.0.7
• Theano version: 0.8.0

Ensure that these versions are compatible for your implementation to run smoothly.

Running the Code

It’s time to put theory into practice! Here are example commands to run each model:

Run GMF

python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run MLP

python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run NeuMF (without pre-training)

python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run NeuMF (with pre-training)

python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrainml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrainml-1m_MLP_[64,32,16,8]_1501652038.h5

Explaining the Code with an Analogy

Think of the different models in NCF as chefs preparing a unique dish at a culinary school. Each chef specializes in different cooking methods:

• The GMF chef perfectly combines ingredients (latent factors) with minimal fuss but focuses on hearty, traditional methods.
• The MLP chef takes a more complex approach, utilizing layers of flavors and techniques, creating a multi-dimensional dining experience.
• The NeuMF chef is a fusion of both techniques, knowing exactly when to preserve tradition and when to innovate, resulting in an extraordinary dish that leverages the strengths of both culinary worlds.

Using Docker for Quick Setup

Docker simplifies the evaluation of models. Follow these steps:

Install Docker Engine

• Ubuntu Installation
• Mac OSX Installation
• Windows Installation

Build the Docker Image

docker build --no-cache=true -t ncf-keras-theano .

Run the Docker Image

Similar to running the code directly, we can run it through Docker:

• GMF:

  docker run --volume=$(pwd):home ncf-keras-theano python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

• MLP:

  docker run --volume=$(pwd):home ncf-keras-theano python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

• NeuMF (without pre-training):

  docker run --volume=$(pwd):home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

• NeuMF (with pre-training):

  docker run --volume=$(pwd):home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrainml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrainml-1m_MLP_[64,32,16,8]_1501652038.h5

Troubleshooting Common Issues

If you encounter issues such as the zsh error (“no matches found”), remember to use single quotation marks for array parameters like this:

--layers '[64,32,16,8]'

For other technical difficulties, ensure your Keras and Theano installations are configured properly. Check your docker setup as well, to validate that paths and volume mounts are correctly set.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.